How does one do an ontological investigation?

It’s a question I’ve been asked several times. Students see ontology papers in venues such as FOIS, EKAW, KR, AAAI, Applied Ontology, or the FOUST workshops and it seems as if all that stuff just fell from the sky neatly into the paper, or that the authors perhaps played with mud and somehow got the paper’s contents to emerge neatly from it. Not quite. It’s just that none of the authors bothered to write a “methods and methodologies” or “procedure” section. That it’s not written doesn’t mean it didn’t happen.

To figure out how to go about doing such an ontological investigation, there are a few options available to you:

• Read many such papers and try to distill commonalities with which one could  reverse engineer a possible process that could have led to those documented outcomes.
• Guess the processes and do something, submit the manuscript, swallow the critical reviews and act upon those suggestions; repeat this process until it makes it through the review system. Then try again with another topic to see if you can do it now by yourself in fewer iterations.
• Try to get a supervisor or a mentor who has published such papers and be their apprentice or protégé formally or informally.
• Enrol in an applied ontology course, where they should be introducing you to the mores of the field, including the process of doing ontological investigations. Or take up a major/minor in philosophy.

Pursuing all options likely will get you the best results. In a time of publish-or-perish, shortcuts may be welcome since the ever greater pressures are less forgiving to learning things the hard way.

Every discipline has its own ways for how to investigate something. At a very high level, it still will look the same: you arrive at a question, a hypothesis, or a problem that no one has answered/falsified/solved before, you do your thing and obtain results, discuss them, and conclude. For ontology, what hopefully rolls out of such an investigation is what the nature of the entity under investigation is. For instance, what dispositions are, a new insight on the transitivity of parthood, the nature of the relation between portions of stuff, or what a particular domain entity (e.g., money, peace, pandemic) means.

I haven’t seen cookbook instructions for how to go about doing this for applied ontology. I did do most of the options listed above: I read (and still read) a lot of articles, conducted a number of such investigations myself and managed to get them published, and even did a (small) dissertation in applied philosophy (mentorships are hard to come by for women in academia, let alone the next stage of being someone’s protégé). I think it is possible to distill some procedure from all of that, for applied ontology at least. While it’s still only a rough outline, it may be of interest to put it out there to get feedback on it to see whether this can be collectively refined or extended.

With X the subject of investigation, which could be anything—a feature such as the colour of objects, the nature of a relation, the roles people fulfill, causality, stuff, collectives, events, money, secrets—the following steps will get you at least closer to an answer, if not finding the answer outright:

1. (optional) Consult dictionaries and the like for what they say about X;
2. Do a scientific literature review on X and, if needed when there’s little on X, also look up attendant topics for possible ideas;
3. Criticise the related work for where they fall short and how, and narrow down the problem/question regarding X;
4. Put forth your view on the matter, by building up the argument step by step; e.g., as follows:
   1. From informal explanation to a possible intermediate stage with sketching a solution (in ad hoc notation for illustration or by abusing ORM or UML class diagram notation) to a formal characterisation of X, or the aspect of X if the scope was narrowed down.
   2. From each piece of informal explanation, create the theory one axiom or definition at a time.
   Either of the two may involve proofs for logical consequences and will have some iterations of looking up more scientific literature to finalise an axiom or definition.

5. (optional) Evaluate and implement.
6. Discuss where it gave new insight, note any shortcomings, and mention new questions it may generate or problem it doesn’t solve yet, and conclude.

For step 3, and as compared to scientific literature I’ve read in other disciplines, the ontologists are a rather blunt critical lot. The formalisation stage in step 4 is more flexible than indicated. For instance, you can choose your logic or make one up [1], but you do need at least something of that (more about that below). Few use tools, such as Isabelle, Prover9, and HeTS, to assist with the logic aspects, but I would recommend you do. Also within that grand step 4, is that philosophers typically would not use UML or ORM or the like, but use total freedom in drawing something, if there’s a drawing at all (and a good number would recoil at the very word ‘conceptual data modeling language’, but that’s for another time), and likewise for many a logician. Here are two sample sequences for that step 4:

A visualization of the ‘one definition or axiom at a time’ option (4b)

A visualization of the ‘iterating over a diagram first’ option (4a)

As an aside, the philosophical investigations are lonesome endeavours resulting in disproportionately more single-author articles and books. This is in stark contrast with ontologies, those artefacts in computing and IT: many of them are developed in teams or even in large consortia, ranging from a few modellers to hundreds of contributors. Possibly because there are more tasks and the scope often may be larger.

Is that all there is to it? Sort of, yes, but for different reasons, there may be different emphases on different components (and so it still may not get you through the publication process to tell the world about your awesome results). Different venues have different scopes, even if they use the same terminology in their respective CFPs. Venues such as KR and AAAI are very much logic oriented, so there must be a formalization and proving interesting properties will substantially increase the (very small) chance of getting the paper accepted. Toning down the philosophical musings and deliberations is unlikely to be detrimental. For instance, our paper on essential vs immutable part-whole relations [2]. I wouldn’t expect the earlier papers, such as on social roles by Masolo et al [3] or temporal mereology by Donnelly and Bittner [4], to be able to make it through in the KR/AAAI/IJCAI venues nowadays (none of the IJCAI’22 papers sound even remotely like an ontology paper). But feel free to try. IJCAI 2023 will be in Cape Town, in case that information would help to motivate trying.

Venues such as EKAW and KCAP like some theory, but there’s got to be some implementation, (plausible) use, and/or evaluation to it for it to have a chance to make it through the review process. For instance, my theory on relations was evaluated on a few ontologies [5] and the stuff paper had the ontology also in OWL, modelling guidance for use, and notes on interoperability [6]. All those topics, which reside in the “step 5” above, come at the ‘cost’ of less logic and less detailed philosophical deliberations—research time and a paper’s page limits do have hard boundaries.

Ontology papers in FOIS and the like prefer to see more emphasis on the theory and what can be dragged in and used or adapted from advances in analytic philosophy, cognitive science, and attendant disciplines. Evaluation is not asked for as a separate item but assumed to be evident from the argumentation. I admit that sometimes I skip that as well when I write for such venues, e.g., in [7], but typically do put some evaluation in there nonetheless (recall [1]). And there still tends to be the assumption that one can write axioms flawlessly and oversee consequences without the assistance of automated model checkers and provers. For instance, have a look at the FOIS 2020 best paper award paper on a theory of secrets [8], which went through the steps mentioned above with the 4b route, and the one about the ontology of competition [9], which took the 4a route with OntoUML diagrams (with the logic implied by its use), and one more on mereology that first had other diagrams as part of the domain analysis to then go to the formalization with definitions and theorems and a version in CLIF [10]. That’s not to say you shouldn’t do an evaluation of sorts (of the variety use cases, checking against requirements, proving consistency, etc.), but just that you may be able to get away with not doing so (provided your argumentation is good enough and there’s enough novelty to it).

Finally, note that this is a blog post and it was not easy to keep it short. Alleys and more explanations and illustrations and details are quite possible. If you have comments on the high-level procedure, please don’t hesitate to leave a comment on the blog or contact me directly!

References

[1] Fillottrani, P.R., Keet, C.M.. An analysis of commitments in ontology language design. Proceedings of the 11th International Conference on Formal Ontology in Information Systems 2020 (FOIS’20). Brodaric, B and Neuhaus, F. (Eds.). IOS Press, FAIA vol. 330, 46-60.

[2] Artale, A., Guarino, N., and Keet, C.M. Formalising temporal constraints on part-whole relations. Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR’08). Gerhard Brewka, Jerome Lang (Eds.) AAAI Press, pp 673-683.

[3] Masolo, C., Vieu, L., Bottazzi, E., Catenacci, C., Ferrario, R., Gangemi, A., & Guarino, N. Social Roles and their Descriptions. Proceedings of the 9th International Conference on Principles of Knowledge Representation and Reasoning (KR’04). AAAI press. pp 267-277.

[4] Bittner, T., & Donnelly, M. A temporal mereology for distinguishing between integral objects and portions of stuff. Proceedings of Association for the Advancement of Artificial Intelligence conference 2007 (AAAI’07). AAAI press. pp 287-292.

 [5] Keet, C.M. Detecting and Revising Flaws in OWL Object Property Expressions. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), A. ten Teije et al. (Eds.). Springer, LNAI 7603, 252-266.

[6] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). Springer LNAI vol. 8876, 209-224.

[7] Keet, C.M. The computer program as a functional whole. Proceedings of the 11th International Conference on Formal Ontology in Information Systems 2020 (FOIS’20). Brodaric, B and Neuhaus, F. (Eds.). IOS Press, FAIA vol. 330, 216-230.

[8] Haythem O. Ismail, Merna Shafie. A commonsense theory of secrets. Proceedings of the 11th International Conference on Formal Ontology in Information Systems 2020 (FOIS’20). Brodaric, B and Neuhaus, F. (Eds.). IOS Press, FAIA vol. 330, 77-91.

[9] Tiago Prince Sales, Daniele Porello, Nicola Guarino, Giancarlo Guizzardi, John Mylopoulos. Ontological foundations of competition. Proceedings of the 10th International Conference on Formal Ontology in Information Systems 2020 (FOIS’18). Stefano Borgo, Pascal Hitzler, Oliver Kutz (eds.). IOS Press, FAIA vol. 306, 96-109.

[10] Michael Grüninger, Carmen Chui, Yi Ru, Jona Thai. A mereology for connected structures. Proceedings of the 11th International Conference on Formal Ontology in Information Systems 2020 (FOIS’20). Brodaric, B and Neuhaus, F. (Eds.). IOS Press, FAIA vol. 330, 171-185.

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What is a pandemic, ontologically?

At some point in time, this COVID-19 pandemic will be over. Each time that thought crossed my mind, there was that little homunculus in my head whispering: but do you know the criteria for when it can be declared ‘over’? I tried to push that idea away by deferring it to a ‘whenever the WHO says it’s over’, but the thought kept nagging. Surely there would be a clear set of criteria lying on the shelf awaiting to be ticked off? Now, with the omicron peak well past us here in South Africa, and with comparatively little harm done in that fourth wave, there’s more talk publicly of perhaps having that end in sight – and thus also needing to know what the decisive factors are for calling it an end.

Then there are the anti-vaxxers. I know a few of them as well. One raged on with the argument that ‘they’ (the baddies in the governments in multiple countries) count the death toll entirely unfairly: “flu deaths count per season in a year, but for covid they keep adding up to the same counter from 2020 to make the death toll look much worse!! Trying to exaggerate the severity!” My response? Duh, well, yes they do count from early 2020, because a pandemic is one event and you count per event! Since the COVID-19 pandemic is a pandemic that is an event, we count from the start until the end – whenever that end is. It hadn’t even crossed my mind that someone wouldn’t count per event but, rather, wanted to chop up an event to pretend it would be smaller than it actually is.

So I did a little digging after all. What is the definition of a pandemic? What are its characteristics? Ontologically, what is that notion of ‘pandemic’, be it according to the analytic philosophers, ontologists, or modellers, or how it may be aligned to some of the foundational ontologies used in ontology engineering? From that, we then should be able to determine when all this COVID-19 has become a ‘is not a pandemic’ (whatever it may be classified into after the pandemic is over).

I could not find any works from the philosophers and theory-focussed ontologists that would have done the work for me already. (If there is and I missed it, please let me know.) Then, to start: what about definitions? There are some, like the recently updated one from dictionary.com where they tried to explain it from a language perspective, and lots of debate and misunderstandings in the debate about defining and describing a pandemic [1]. The WHO has descriptions, but not a clear definition, and pandemic phases. Formulations of definitions elsewhere vary slightly as well, except for the lowest common denominator: it’s a large epidemic.

Ontologically, that is an entirely unsatisfying answer. What is ‘large’? Some, like the CDC of the USA qualified it somewhat: it’s spread over the world or at least multiple regions and continents, and in those areas, it usually affects many people. The Australian Department of Health adds ‘new disease’ to it. Now we’re starting to get somewhere with inclusion of key properties of a pandemic. Kelly [2] adds another criterion to it, albeit focussed on influenza: besides worldwide/very wide area and  affecting a large number of people, “almost simultaneous transmission takes place worldwide” and thus for a part of the world, there is an out-of-season influenza virus transmission.

Image credits: Miroslava Chrienova, taken from this page.

The best resource of all from an ontologists’ perspective, is a very clear, well-written, perspective article written by Morens, Folkers and Fauci – yes, that Fauci from the CDC – in the Journal of Infectious Diseases that, in their lack of wisdom, keeps the article paywalled (it somehow made it onto the webarchive with free access here anyhow). They’re experts and they trawled the literature to, if not define a pandemic, then at least describe it through trying to list the characteristics and the merits, or demerits, thereof. They are, in short, and with my annotation on what sort of attribute (/feature/characteristic, as loosely used term for now) it is:

1. Wide geographic extension; as aforementioned. That’s a scale or ‘fuzzy’ (imprecise in some way) feature, i.e., without a crisp cut-off point when ‘wide’ starts or ends.
2. Disease movement, i.e., there’s some transmission going on from place to place and that can be traced. That’s a yes/no characteristic.
3. High attack rates and explosiveness, i.e., lots of people affected in a short timespan. There’s no clear cut-off point on how fast the disease has to spread for counting as ‘fast spreading’, so a scale or fuzzy feature.
4. Minimal population immunity; while immunity is a “relative concept” (i.e., you have it to a degree), it’s a clear notion for a population when that exists or not; e.g., it certainly wasn’t there when SARS-CoV-2 started spreading. It is agnostic about how that population immunity is obtained. This may sound like a yes/no feature, perhaps, but is fuzzy, because practically we may not know and there’s for sure a grey area thanks to possible cross-immunity (natural or vaccine-induced) and due to the extent of immune-evasion of the infectious agent.
5. Novelty; the term speaks for itself, and clearly is a yes/no feature as well. It seems to me like ‘novel’ implies ‘minimal population immunity’, but that may not be the case.
6. Infectiousness; it’s got to be infectious, and so excluding non-infectious things, like obesity and smoking. Clear yes/no.
7. Contagiousness; this may be from person to person or through some other medium (like water for cholera). Perhaps as an attribute with categorical values; e.g., human-to-human, human-animal intermediary (e.g., fleas, rats), and human-environment (notably: water).
8. Severity; while the authors note that it’s not typically included, historically, the term ‘pandemic’ has been applied more often for diseases that are severe or with high fatality rates (e.g., HIV/AIDS) than for milder ones. Fuzzy concept for which a scale could be used.

And, at the end of their conclusions, “In summary, simply defining a pandemic as a large epidemic may make ultimate sense in terms of comprehensibility and consistency. We also suggest that use of the term is best reserved for infectious diseases that share many of the same epidemiologic features discussed above” (p1020), largely for simplifying it to the public, but where scientists and public health officials would maintain their more precise consensus understanding of the complex scientific concept.

Those imprecise/fuzzy properties and lack of clarity of cut-off points bug the epidemiologists, because they lead to different outcomes of their prediction models. From my ontologist viewpoint, however, we’re getting somewhere with these properties: SARS-CoV-2, at least early in 2020 when the pandemic was declared, ticked all those eight boxes and so any reasoner would classify the disease it causes, COVID-19, as a pandemic. Now, in early 2022 with/after the omicron variant of concern? Of those eight properties, numbers 4 and 8 much less so, and number 5 is the million-dollar-question two years into the pandemic. Either way, considering all those properties of a pandemic that have passed the revue here so far, calling an end to the pandemic is not as trivial is it initially may have sounded like. WHO’s “post pandemic period” phase refers to “levels seen for seasonal influenza in most countries with adequate surveillance”. That is a clear specification operationally.

Ontologically, if we were to take these eight properties at face value, the next question then is: are all eight of them combined the necessary and sufficient conditions, or are some of them ‘more essential’ for calling it a pandemic, and the other ones would then be optional features? Etymologically, the pan in pandemic means ‘all’, so then as long as it rages across the world, it would remain a pandemic?

Now that things get ontologically more interesting, the ontological status. Informally, an epidemic is an occurrence (read: instance/individual entity) of an infectious disease at a particular time (read: an unspecified duration of time, not an instant) and that affects some community (be that a community of humans, chicken, or whatever other organisms that live in a community), and pandemic, as a minimum, extends the region that it affects and amount of organisms infected, and then some of those other features listed above.

A pandemic is in the same subject domain as an infectious disease, and so we can consult the OBO Foundry and see what they did, or first start with just the main BFO categories for a general sense of what it would align to. With our BFO Classifier, I get as far as process:

As to the last (optional) question: could one argue that a pandemic is a collection of disjoint part-processes? Not if the part-processes all have to be instances of different types of processes. The other loose end is that BFO’s processes need not have an end, but pandemics do. For now, what’s the most relevant is that the pandemic is distinctly in the occurrent branch of BFO, and occurrents have temporal parts.

Digging further into the OBO Foundry, they indeed did quite some work on infectious diseases and COVID-19 already [4], and following the trail from their Figure 1 (see below): disposition is a realizable entity is a specifically dependent continuant is a continuant; infectious disease course is a disease course is a process is an occurrent; and “realizable entity comes to be realized in the course of the process”.

Source: Figure 1 of [4].

In that approach, COVID-19 is the infectious disease being realised in the pandemic we’re in at the moment, with multiple infectious disease courses in humans and a few other animals. But where does that leave us with pandemic? Inspecting the Infectious Disease Ontology (IDO) since the article does not give a definition, infectious disease epidemic and infectious disease pandemic are siblings of infectious disease course, where disease course is described as “Totality of all processes through which a given disease instance is realized.” (presumably the totality of all processes in one human where there’s an instance of, say, COVID-19). Infectious disease pandemic is an atomic class with no properties or formal definitions, but there’s an annotation with a definition. Nice try; won’t work.

What’s the problem? There are three. The first, and key, problem is that pandemic is stated to be a collection of epidemics, but i) collections of individual things (collectives, aggregates) are categorically different kind of entities than individual things, and ii) epidemic and pandemic are not categorically different things. Not just that, there’s a fiat boundary (along a continuum, really) between an epidemic evolving into becoming a pandemic and then subsiding into separate epidemics. A comparatively minor, or at least secondary, issue is how to determine the boundary of one epidemic from another to be able to construct a collective, since, more fundamentally: what are the respective identities of those co-occurring epidemics? One can’t get collections of things we can’t quite identify. For instance, is it one epidemic in two places that it jumped to, or do they count as two then, and what when two separate ones touch and presumably merge to become one large one? The third issue, and also minor for the current scope, is the definition for epidemic in the ontology’s annotation field, talking of “statistically significant increase in the infectious disease incidence” as determiner, but actually it’s based on a threshold.

Let’s try DOLCE as foundational ontology and see what we get there. With the DOLCE Decision Diagram [5], pandemic ends up as: Is [pandemic] something that is happening or occurring? Yes (perdurant – alike BFO’s occurrent). Are you able to be present or participate in [a pandemic]? Yes (event). Is [a pandemic] atomic, i.e., has no subdivisions of it and has a definite end point? No (accomplishment). Not the greatest word choice to say that a pandemic is an accomplishment – almost right up there with the DOLCE developers’ example that death is an achievement – but it sure is an accomplishment from the perspective of the infectious agent. The nice thing of dolce:accomplishment over  bfo:process is that it entails there’s a limited duration to it (DOLCE also has process that also can go on and on and on).

The last question in both decision diagrams made me pause. The instances of COVID-19 going around could possibly be going around after the pandemic is over, uninterrupted in the sense that there is no time interval where no-one is infected with SARS-CoV-2, or it could be interrupted with later flare-ups if it’s still SARS-CoV-2 and not substantially different, but the latter is a grey area (is it a flare-up or a COVID-2xxx?). The latter is not our problem now. The former would not be in contradiction with pandemic as accomplishment, because COVID-19-the-pandemic and COVID-19-the-disease are two different things. (How those two relate can be a separate story.)

To recap, we have pandemic as an occurrent/perdurant entity unfolding in time and, depending on one’s foundational ontology, something along the line of accomplishment. For an epidemic to be classified as a pandemic, there are a varying number of features that aren’t all crisp and for which the fuzzy boundaries haven’t been set.

To sketch this diagrammatically (hence, informally), it would look something like this:

where the clocks and the DEX and DEV arrows are borrowed from the TREND temporal conceptual data modelling language [6]: Epidemic and Pandemic are temporal entities, DEX (+dashed arrow) verbalised is “An epidemic may also become a pandemic” and DEV (+solid arrow): “Each pandemic must evolve to epidemic ceasing to be a pandemic” (hiding the logic at the back-end).

It isn’t a full answer as to what a pandemic is ontologically – hence, the title of the blog post still has that question mark – but we can already clear up the two issues from the introduction of this post, as follows.

Consequences

We already saw that with any definition, description, and list of properties proposed, there is no unambiguous and certain definite endpoint to a pandemic that can be deterministically computed. Well, other than the extremes of either 100% population immunity or the affected species is extinct such that there is no single instance of a disease course (in casu, of COVID-19) either way. Several measured values of the scales for the fuzzy variables will go down and immunity increase (further) as the pandemic unfolds, and then the pandemic phase is over eventually. Since there are no thresholds defined, there likely will be people who are forever disagreeing on when it can be called over. That is inherent in the current state of defining what a pandemic is. Perhaps it now also makes you appreciate the somewhat weak operational statement of the WHO post-pandemic period phase – specifying anything better is fraught with difficulties to date and unlikely to ever make everybody happy.

There’s that flawed argument of the anti-vaxxer to deal with still. Flu epidemics last about 10 weeks, on average [7]. They happen in the winter and in the  northern hemisphere that may cross a New Year (although I can’t remember that has ever happened in all the years I’ve lived in Europe). And yet, they also count per epidemic and not per calendar year. School years run from September to July, which provides a different sort of year, and the flu epidemics there are typically reported as ‘flu season 2014/2015’, indicating just that. Because those epidemics are short-lived, you typical get only one of those in a year, and in-season only.

Contrast this with COVID-19: it’s been going round and round and round since late December 2019, with waves and lulls for all countries, regions, and continents, but never did it stop for a season in whole regions or continents. Most countries come close to a stop during a lull at some point between the waves; for South Africa, according to worldometers, the lowest 7-day moving average since the first wave in 2020 was 265 recorded infections per day, on 7 November 2021. Any out-of-season waves? Oh yes – beta came along in summer last year and it was awful; at least for this year’s summer we got a relatively harmless omicron. And it’s not just South Africa that has been having out-of-season spikes. Point is, the COVID-19 pandemic ‘accomplishment’ wasn’t over within the year – neither a calendar year nor a northern hemisphere school year – and so we keep counting with the same counter for as long as the event takes until the pandemic as event is over. There’s no nefarious plot of evil controlling scaremongering governments, just a ‘demic that takes a while longer than we’ve been used to until 2019.

In closing, it is, perhaps, not the last word on the ontological status of pandemic, but I hope the walkthrough provided a little bit of clarity in the meantime already.

References

[1] Doshi, P. The elusive definition of pandemic influenza. Bulletin of the World Health Organization,  2011, 89:532–538

[2] Kelly, H. The classical definition of a pandemic is not elusive. Bulletin of the World Health Organization, 2011, 89 (‎7)‎, 540 – 541.

[3] Morens, DM, Folkers, GK, Fauci, AS. What Is a Pandemic? The Journal of Infectious Diseases, 2009, 200(7): 1018-1021.

[4] Babcock, S., Beverley, J., Cowell, L.G. et al. The Infectious Disease Ontology in the age of COVID-19. Journal of Biomedical Semantics, 2021, 12, 13.

[5] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. 2013.

[6] Keet, C.M., Berman, S. Determining the preferred representation of temporal constraints in conceptual models. 36th International Conference on Conceptual Modeling (ER’17). Springer LNCS 10650, 437-450. 6-9 Nov 2017, Valencia, Spain.

[7] Fleming DM, Zambon M, Bartelds AI, de Jong JC. The duration and magnitude of influenza epidemics: a study of surveillance data from sentinel general practices in England, Wales and the Netherlands. European Journal of Epidemiology, 1999, 15(5):467-73.

My road travelled from microbiology to computer science

From bites to bytes or, more precisely, from foods to formalisations, and that sprinkled with a handful of humanities and a dash of design. It does add up. The road I travelled into computer science has nothing to do with any ‘gender blabla’, nor with an idealistic drive to solve the world food problem by other means, nor that I would have become fed up with the broad theme of agriculture. But then what was it? I’m regularly asked about that road into computer science, for various reasons. There are those who are curious or nosy, some deem it improbable and that I must be making it up, and yet others chiefly speculate about where I obtained the money from to pay for it all. So here it goes, in a fairly large write-up since I did not take a straight path, let alone a shortcut.

If you’ve seen my CV, you know I studied “Food Science, free specialisation” at Wageningen University in the Netherlands. It is the university to go to for all things to do with agriculture in the broad sense. Somehow I made it into computer science, but it was not there. The motivation does come from there, thanks to it being at the forefront of science and such has an ambiance that facilitates exposure to a wide range of topics and techniques within the education system and among fellow students. (Also, it really was the best quality education I ever had, which deserves to be said—and I’ve been around to have ample comparison material.)

And yet.

Perhaps it is conceivable to speculate that all the hurdles with mathematics and PC use when I was young were the motivation to turn to computing. Definitely not. Instead, it happened when I was working on my last, and major, Master’s thesis in the Molecular Ecology section of the Laboratory of Microbiology at Wageningen University, having drifted away a little from microbes in food science.

My thesis topic was about trying to clean up chemically contaminated soil by using bacteria that would eat the harmful compounds, rather than cleaning up the site by disrupting the ecosystem with excavations and chemical treatments of the soil. In this case, it was about 3-chlorobenzoate, which is an intermediate degradation product from, mainly, spilled paint that had been going on since the 1920s and said molecule substantially reduces growth and yield of maize, which is undesirable. I set out to examine a bunch of configurations of different amounts of 3-chlorobenzoate in the soil together with the Pseudomonas B13 bacteria and distance to the roots of the maize plants and their effects on the growth of the maize plants. The bacteria were expected to clean up more of the 3-chlorobenzoate in the area nearby the roots (the rhizosphere), and there were some questions about what the bacteria would do once the 3-chlorobenzoate ran out (mainly: will they die or feed on other molecules?).

The birds-eye view still sounds interesting to me, but there was a lot of boring work to do to find the answer. There were days that the only excitement was to open the stove to see whether my beasts had grown on the agar plate in the petri dish; if they had (yay!), I was punished with counting the colonies. Staring at dots on the agar plate in the petri dish and counting them. Then there were the analysis methods to be used, of which two turned out to be crucial for changing track, mixed with a minor logistical issue to top it off.

First, there was the PCR technique to sequence genetic material, which by now during COVID-19 times, may be a familiar term. There are machines that do the procedure automatically. In 1997, it was still a cumbersome procedure, which took about a day near non-stop work to sequence the short ribosomal RNA (16S rRNA) strand that was extracted from the collected bacteria. That was how we could figure out whether any of those white dots in the petri dish were, say, the Pseudomonas B13 I had inoculated the soil with, or some other soil bacteria. You extract the genetic material, multiply it, sequence it and then compare it. It was the last step that was the coolest.

The average number of base pairs of the 16S rRNA of a bacterium is around 1500 base pairs which is represented as a sequence of some 1500 capital letters consisting of A’s, C’s, G’s, and U’s. For comparison: the SARS-CoV-2 genome is about 30000 base pairs. You really don’t want to compare either one by hand against even one other similar sequence of letters, let alone manually checking your newly PCR-ed sequence against many others to figure out which bacteria you likely had isolated or which one is phylogenetically most closely related. Instead, we sent the sequence, as a string of flat text with those ACGU letters, to a database called the RNABase and we received an answer with a list of more or less likely matches within a few hours to a day, depending on the time of submitting it to the database.

It was like magic. But how did it really do that? What is a database? How does it calculate the alignments? And since it can do this cool stuff that’s not doable by humans, what else can you do with such techniques to advance our knowledge about the world? How much faster can science advance with these things? I wanted to know. I needed to know.

The other technique I had to work with was not new to me, but I had to scale it up: the High-Performance Liquid Chromatography (HPLC). You give the machine a solution and it separates out the component molecules, so you can figure out what’s in the solution and how much of it is in there. Different types of molecules stick to the wall of the tube inside the machine at different places. The machine then spits out the result as a graph, where different peaks scattered across the x axis indicate different substances in the solution and the size of the peak indicates the concentration of that molecule in the sample.

I had taken multiple soil samples closer and father away from the rhizosphere of different boxes with maize plants with different treatments of the soil, rinsed it and tested the solution in the HPLC. The task then was to compare the resulting graphs to see if there was a difference in treatment. Having printed them all out, they covered a large table of about 1.5 by 2 meter, and I had to look closely at them and try to do some manual pattern matching on the shape and size of the graphs and sub-graphs. There was no program that could compare graphs automatically. I tried to overlay printouts and hold them in front of the ceiling light. With every printed graph about the size of 20x20cm, you can calculate how many I had and how many 1-by-1 comparisons that amounts to (this is left as an exercise to the reader). It felt primitive, especially considering all the fancy toys in the lab and on the PC. Couldn’t those software developers not also develop a tool to compare graphs?! Now that would have been useful. But no. If only I could develop such a useful tool myself; then I would not have to wait on the software developers until they care to develop it.

On top of that manual analysis was that it seemed unfair that I had to copy the data from the HPLC machine in the basement of the building onto a 3.5 inch floppy disk and walk upstairs to the third floor to the shared MSc thesis students’ desktop PCs to be able to process it, whereas the PCR data was accessible from my desktop PC even though the PCR machine was on the ground floor. The PC could access the internet and present data from all over the world, even, so surely it should be able to connect to the HPLC downstairs?! Enter questions about computer networks.

The first step in trying to get some answers, was to inquire with the academics in the department. “Maybe there’s something like ‘theoretical microbiology’, or whatever it’s called that focuses on data analysis and modelling of microbiology? It is the fun part of the research—and avoids lab work?”, I asked my supervisor and more generally in the lab. “Not really,”, was the answer, continuing “ok, sure, there is some, but theory-only without the evidence from experiments isn’t it.” Despite all the advanced equipment, of which computing is an indispensable component, they still deemed that wetlab research trumped solely theory and computing. “Those technologies are there to assist answering faster the new and more advanced questions, but not replace the processes”, I was told.

Sigh. Pity. So be it, I supposed. But I still wanted answers to those computing questions. I also wanted to do a PhD in microbiology and then probably move to some other discipline, since I sensed that possibly after another 4-6 years I might become bored with microbiology. Then there was the logistical issue that I still could not walk well, which made wetlab work difficult; hence, it would make obtaining a PhD scholarship harder. Lab work was a hard requirement for a PhD in microbiology and it wasn’t exactly the most exciting part of studying bacteria. So, I might as well swap to something else straight away then. Since there were those questions in computing that I wanted answers to, there we have the inevitable conclusion to move to greener, or at least as green, pastures.

***

How to obtain those answers in computing? Signing up for a sort of ‘top up’ degree for the computing aspects would be nice, so as to do that brand new thing called bioinformatics. There were no such top-up degrees in the Netherlands at the time and the only one that came close was a full degree in medical informatics, which is not what I wanted. I didn’t want to know about all the horrible diseases people can get.

The only way to combine it, was to enrol in the 1st year of a degree in computing. The snag was the money. I was finishing up my 5 years of state funding for the master’s degree (old system, so it included the BSc) and the state paid for only one such degree. The only way to be able to do it, was to start working, save money, and pay for it myself at some point in the near future once I’d have enough money. Going into IT in industry out in the big wide world sounded somewhat interesting as second-choice option, since it should be easier with such skills to work anywhere in the world, and I still wanted to travel the world as well.

Once I finished the thesis in molecular ecology and graduated with a master’s degree in January 1998, I started looking for work whilst receiving unemployment benefit. IT companies only offered ‘conversion’ courses, such as a crash course in Cobol—the Y2K bug was alive and well—or some IT admin course, including Microsoft Certified System Engineer program (MCSE), with the catch that you’d have to keep working for the IT company for 3 years to pay off the debt of that training. That sounded like bonded labour and not particularly appealing.

Some day flicking through the newspapers on the lookout for interesting job offers, an advertisement caught my eye: a conversion course over a year for an MCSE consisting of five months full-time training and the rest of the year a practice period in industry whilst maintaining one’s unemployment benefit whose amount was just about sufficient to get by, and then all was paid off. A sizeable portion of funding came from the European Union. The programme was geared toward giving a second chance for basket cases, such as the long-term unemployed and the disabled. I was not a basket case, not yet at least. I tried nonetheless, applied for a position, and was invited for an interview. My main task was to try to convince them that I was basket case-like enough to qualify to be accepted in the programme, but good enough to pass fast and with good marks. The arguments worked and I was accepted for the programme. A foothold in the door.

We were a class of 16 people, 15 men and me the only woman. I completed the MCSE successfully, and then I also completed a range of other vocational training courses whilst employed in various IT jobs. Unix system administration, ITIL service management, a bit of Novell Netware and Cisco, and some more online self-study training sessions, which were all paid for by the companies I was employed at. The downside with those trainings, is that they all were, in my humble opinion, superficial and the how-to technology changes fast and the prospect or perpetual rote learning did not sound appealing to me. I wanted to know the underlying principles so that I wouldn’t have to keep updating myself with the latest trivia modification in an application. It was time to take the next step.

I was working for Eurologic Systems in Dublin, Ireland, at the time as a systems integration test engineer for fibre channel storage enclosures, which are boxes with many hard drives stacked up and connected for fast access to lots of data stored on the disks. They were a good employer, but they had only few training opportunities since it was an R&D company with experienced and highly educated engineers. I asked HR if I could sign up elsewhere, with, say, the Open University, and that they’d pay for some of it, maybe? “Yes,” the humane HR lady said, “that’s a good idea, and we’ll pay for every course you pass whilst in our employment.” Deal!

So, I enrolled with the Open University UK. I breezed through my first year even though I had skipped their 1st year courses and jumped straight into 2nd year courses. My second year went just as smoothly. The third year I paid myself, since I had opted for voluntary redundancy and was allowed to take it in the second round, since I wanted to get back on track of my original plan to go into bioinformatics. The dotcom bubble had burst and Eurologic could not escape some of its effects. While they were not fond of seeing me go, they knew I’d leave soon anyway and they were happy to see that the redundancy money would be put to good use to finish my Computing & IT degree. With that finished, I’d be able to finally do the bioinformatics that I was after since 1997, or so I thought.

My honours project was on database development, with a focus on conceptual data modelling languages. I rediscovered the Object-Role Modelling language from the lecture notes of the Saxion University of Applied Sciences that I had bought out of curiosity when I did the aforementioned MCSE course (in Enschede, the Netherlands). The database was about bacteriocins, which are produced by bacteria and they can be used in food for food safety and preservation. A first real step into bioinformatics. Bacteriocins have something to do with genes, too, and in searching for conceptual models about genes, I had stumbled into a new world in 2003, one with the Gene Ontology and the notion of ontologies to solve the data integration problem. Marking and marks processing took a bit longer than usual that year (the academics were on strike), and I was awarded the BSc(honours) degree (1st class) in March 2004. By that time, there were several bioinformatics conversion courses available. Ah, well.

The long route taken did give me some precious insight that no bioinformatics conversion top-up degree can give: a deeper understanding of indoctrination into disciplinary thinking and ways of doing science. That is, on what the respective mores are, how to question, how to identify a problem, looking at things, ways of answering questions and solving problems. Of course, when there’s, say, an experimental method, the principles of the methods are the same—hypothesis, set up experiment, do experiment, check results against hypothesis—as are some of the results processing tools the same (e.g., statistics), but there are substantive differences. For instance, in computing, you break down to problem, isolate it, and solve that piece of something that’s all human-made. In microbiology, it’s about trying to figure out how nature works, with all its interconnected parts that may interfere and complicate the picture. In the engineering side of food science, it was more along the line of, once we figure out what it does and what we really need, can we find something that does what we need or can we me make it do it to solve the problem? It doesn’t necessarily mean one is less cool; just different. And hard to explain to someone who has ever studied only one degree in one discipline, most of whom invariably have the ‘my way or the highway’ attitude or think everyone is homologous to them. If you manage to create the chance to do a second full degree, take it.

***

Who am I to say that a top-up degree is unlike the double indoctrination into a discipline’s mores? Because I also did a top-up degree, in yet another discipline. Besides studying for the last year in Computing & IT with a full-time load, I had also signed up for a conversion Master’s of Arts in Peace & Development studies at the University of Limerick, Ireland. The Computing & IT degree didn’t seem like it would be a lot of work, so I was looking for something to do on the side. I had also started exploring what to do after completing the degree, and in particular to maybe sign up for a masters or PhD in bioinformatics. And so it was that I stumbled upon the information about the Masters of Arts in Peace & Development studies in the postgraduate prospectus. Reading up on the aims and the courses, this coursework and dissertation masters looked like it might actually help me answer some questions I had that were nagging since I spent some time in Peru. Before going to Peru, I was a committed pacifist; violence doesn’t solve problems. Then Peru’s Moviemento Revolucionario de Tupac Amaru (MRTA) hijacked the Japanese embassy in Lima in late 1996 when I was in Lima. They were trying to draw attention to the plight of the people in the Andes and demanded more resources and investments there. I’d seen the situation there, with its malnutrition, limited potable water, and limited to no electricity, which was in stark contrast to the coastal region. The Peruvians I spoke to did not condone the MRTA’s methods, but they had a valid point, or so went the consensus. Can violence ever be justified? Maybe violence could be justified if all else had failed in trying to address injustices? If it is used, will it lead to something good, or merely a set-up for the next cycle of violence and oppression?

I clearly did not have a Bachelor of arts, but I had done some courses roughly in that area in my degree in Wageningen and had done a range of extra-curricular activities. Perhaps that, and more, would help me persuade the selection committee? I put it all in detail in the application form in the hope it would increase my chances to try to make it look like I could pull this off and be accepted into the programme. I was accepted into the programme. Yay. Afterwards, I heard from one of the professors that it had been an easy decision, “since you already have a Masters degree, of science, no less”. Also this door was opened thanks to that first degree I had obtained that was paid for by the state merely because I qualified for the tertiary education. The money to pay for this study came from my savings and the severance package from Eurologic. I had earned too much money in industry to qualify for state subsidy in Ireland; fair enough.

Doing the courses, I could feel I was missing the foundations, both regarding the content of some established theories here and there and in tackling things. By that time, I was immersed in computing, where you break down things in smaller sub-components and that systematising is also reflected in the reports you write. My essays and reports have sections and subsections and suitably itemised lists—Ordnung muss sein. But no, we’re in a fluffy humanities space and it should have been ‘verbal diarrhoea’. That was my interpretation of some essay feedback I had received, which claimed that there was too much structure and that it should have been one long piece of text without visually identifiable begin, middle, and end. That was early in the first semester. A few months into the programme, I thought that the only way I’d be able to pull off the dissertation, was to drag the topic as much as I could into an area that I was comparatively good at: modelling and maths.

That is: to stick with my disciplinary indoctrinations as much as possible, rather than fully descend into what to me still resembled mud and quicksand. For sure, there’s much more to the humanities than meets an average scientist’s eye, and I gained an appreciation of it during that degree, but that does not mean I was comfortable with it. In addition, for thesis topic choice, there were still the ‘terrorists’ I was looking for an answer to. Combine the two, and voila, my dissertation topic: applying game theory to peace negotiations in the so-called ‘terrorist theatre’. Prof. Moxon-Browne was not only a willing, but also eager, supervisor, and a great one at that. The fact that he could not wait to see my progress was a good stimulator to work and achieve that progress.

In the end, the dissertation had some ‘fluffy’ theory, some mathematical modelling, and some experimentation. It looked into three party negotiations cf. the common zero-sum approach in the literature: the government and two aggrieved groups, of which one was the politically-oriented one and the other one the violent one. For instance, in the case of South Africa, the Apartheid government on the one side and the ANC and the MK on the other side, and in case of Ireland, the UK/Northern Ireland government, Sinn Fein and the IRA. The strategic benefits of who teams up with whom during negotiations, if at all, depends on their relative strength: mathematically, in several identified power-dynamic circumstances, an aggrieved participant could obtain a larger slice of the pie for the victims if they were not in a coalition than if they were, and the desire, or not, for a coalition among aggrieved groups depended on their relative power. This deviated from the widespread assumption at the time that said that the aggrieved groups should always band together. I hoped it would still be enough for a pass.

It was awarded a distinction. It turned out that my approach was fairly novel. Perhaps therein lies a retort argument for the top-up degrees against the ‘do both’ advice I mentioned before: a fresh look on the matter, if not interdisciplinarity or transdisciplinarity. I can see it also with the dissertation topics of our conversion Masters in IT students as well. They’re all interesting and topics that perhaps no disciplinarian would have produced.

***

The final step, then. With a distinction in the MA in Peace & Development in my pocket and a first in the BSc(honours) in CS&IT at around the same time, what next? The humanities topics were becoming too depressing even with a detached scientific mind—too many devastating problems and too little agency to influence—and I had worked toward the plan to go into bioinformatics for so many years already. Looking for jobs in bioinformatics, they all demanded a PhD. With the knowledge and experience amassed studying for the two full degrees, I could do all those tasks they wanted the bioinformatician to do. However, without meeting that requirement for a PhD, there was no chance I’d make it through the first selection round. That’s what I thought at the time. I tried 1-2 regardless—reject because no PhD. Maybe I should have tried and applied more widely nonetheless, since, in hindsight, it was the system’s way of saying they wanted someone well-versed in both fields, not someone trained to become an academic, since most of those jobs are software development jobs anyway.

Disappointed that I still couldn’t be the bioinformatician I thought I would be able to be after those two degrees, I sighed and resigned to the idea that, gracious sakes, I’ll get that PhD, too, then, and defer the dream a little longer.

In a roundabout way I ended up at the Free University of Bozen-Bolzano (FUB), Italy. They paid for the scholarship and there was generous project funding to pay for conference attendance. Meanwhile in the bioinformatics field, things had moved on from databases for molecular biology to bio-ontologies to facilitate data integration. The KRDB research centre at FUB was into ontologies, but then rather from the logic side of things. Fairly soon after my commencement with the PhD studies, my supervisor, who did not even have a PhD in Computer Science, told me in no unclear terms that I was enrolled in a PhD in computer science, that my scientific contributions had to be in computer science, and if I wanted to do something in ‘bio-whatever’, that was fine, but that I’d have to do that in my own time. Crystal clear.

The `bio-whatever’ petered out, since I had to step up the computer science content because I had only three years to complete the PhD. On the bright side, passion will come the more you investigate something. Modelling, with some examples in bio, and ontologies and conceptual modelling it was. I completed my PhD in three year(-ish); fully indoctrinated in the computer science way. Journey completed.

***

I’ve not yet mentioned the design I indicated at the start of the blog post. It has nothing to do with moving into computer science. At all. Weaving in the interior design into the narrative didn’t work well, and it falls under the “vocational training courses whilst employed in various IT jobs” phrase earlier on. The costs of the associate diploma at the Portobello Institute in Dublin? I earned most of the costs (1200 pound or so? I can’t recall exactly, but it was somewhere between 1-2K) together in a week: we got double pay for working a shift on New Year (the year 2000 no less) and then I volunteered for the double pay for 12h shifts instead of regular 8h shifts for the week thereafter. One week extra work for an interesting hobby in the evening hours for a year was a good deal in my opinion, and it allowed me to explore whether I liked the topic as much as I thought I might in secondary school. I passed with a distinction and also got Rhodec certified. I still enjoy playing around with interiors, as hobby, and have given up the initial idea (in 1999) to use IT with it, since tangible samples work fine.

So, yes, I really have completed degrees in science, engineering, and political science straddling into humanities, and a little bit of the arts. A substantial chunck was paid for by the state (‘full scholarships’), companies chimed in as well, and I paid some of it from my hard earned money. On the motivations for the journey: I hope I made that clear despite cutting out some text in an attempt to reduce the post’s length. (Getting into university in the first place and staying in academia after completing a PhD are two different stories altogether, and left for another time.)

I still have many questions, but I also realise that many will remain unanswered even if the answer is known to humanity already, since to live means it’s finite and there’s simply not enough time to learn everything. In any case: do study what you want, not what anyone tells you to study. If the choice is a study or, say, a down payment on a mortgage for a house, then if completing the study will give good prospects and relieves you from a job you are not aiming for, go for it—that house may be bought later and be a tad bit smaller. It’s your life you’re living, not someone else’s.

Some experiences on making a textbook available

I did make available a textbook on ontology engineering for free in July 2018. Meanwhile, I’ve had several “why did you do this and not a proper publisher??!?” I had tried to answer that already in the textbook’s FAQ. Turns out that that short answer may be a bit too short after all. So, here follows a bit more about that.

The main question I tried to answer in the book’s FAQ was “Would it not have been better with a ‘proper publisher’?” and the answer to that was:

Probably. The layout would have looked better, for sure. There are several reasons why it isn’t. First and foremost, I think knowledge should be free, open, and shared. I also have benefited from material that has been made openly available, and I think it is fair to continue contributing to such sharing. Also, my current employer pays me sufficient to live from and I don’t think it would sell thousands of copies (needed for making a decent amount of money from a textbook), so setting up such a barrier of high costs for its use does not seem like a good idea. A minor consideration is that it would have taken much more time to publish, both due to the logistics and the additional reviewing (previous multi-author general textbook efforts led to nothing due to conflicting interests and lack of time, so I unlikely would ever satisfy all reviewers, if they would get around reading it), yet I need the book for the next OE installment I will teach soon.

Ontology Engineering (OE) is listed as an elective in the ACM curriculum guidelines. Yet, it’s suited best for advanced undergrad/postgrad level because of the prerequisites (like knowing the basics of databases and conceptual modeling). This means there won’t be big 800-students size classes all over the world lining up for OE. I guess it would not go beyond some 500-1000/year throughout the world (50 classes of 10-20 computer science students), and surely not all classes would use the textbook. Let’s say, optimistically, that 100 students/year would be asked to use the book.

With that low volume in mind, I did look up the cost of similar books in the same and similar fields with the ‘regular’ academic publishers. It doesn’t look enticing for either the author or the student. For instance this one from Springer and that one from IGI Global are all still >100 euro. for. the. eBook., and they’re the cheap ones (not counting the 100-page ‘silver bullet’ book). Handbooks and similar on ontologies, e.g., this and that one are offered for >200 euro (eBook). Admitted there’s the odd topical book that’s cheaper and in the 50-70 euro range here and there (still just the eBook) or again >100 as well, for a, to me, inexplicable reason (not page numbers) for other books (like these and those). There’s an option to publish a textbook with Springer in open access format, but that would cost me a lot of money, and UCT only has a fund for OA journal papers, not books (nor for conference papers, btw).

IOS press does not fare much better. For instance, a softcover version in the studies on semantic web series, which is their cheapest range, would be about 70 euro due to number of pages, which is over R1100, and so again above budget for most students in South Africa, where the going rate is that a book would need to be below about R600 for students to buy it. A plain eBook or softcover IOS Press not in that series goes for about 100 euro again, i.e., around R1700 depending on the exchange rate—about three times the maximum acceptable price for a textbook.

The MIT press BFO eBook is only R425 on takealot, yet considering other MIT press textbooks there, with the size of the OE book, it then would be around the R600-700. Oxford University Press and its Cambridge counterpart—that, unlike MIT press, I had checked out when deciding—are more expensive and again approaching 80-100 euro.

One that made me digress for a bit of exploration was Macmillan HE, which had an “Ada Lovelace day 2018” listing books by female authors, but a logics for CS book was again at some 83 euros, although the softer area of knowledge management for information systems got a book down to 50 euros, and something more popular, like a book on linguistics published by its subsidiary “Red Globe Press”, was down to even ‘just’ 35 euros. Trying to understand it more, Macmillan HE’s “about us” revealed that “Macmillan International Higher Education is a division of Macmillan Education and part of the Springer Nature Group, publishers of Nature and Scientific American.” and it turns out Macmillan publishes through Red Globe Press. Or: it’s all the same company, with different profit margins, and mostly those profit margins are too high to result in affordable textbooks, whichever subsidiary construction is used.

So, I had given up on the ‘proper publisher route’ on financial grounds, given that:

• Any ontology engineering (OE) book will not sell large amounts of copies, so it will be expensive due to relatively low sales volume and I still will not make a substantial amount from royalties anyway.
• Most of the money spent when buying a textbook from an established publisher goes to the coffers of the publisher (production costs etc + about 30-40% pure profit [more info]). Also, scholarships ought not to be indirect subsidy schemes for large-profit-margin publishers.
• Most publishers would charge an amount of money for the book that would render the book too expensive for my own students. It’s bad enough when that happens with other textbooks when there’s no alternative, but here I do have direct and easy-to-realise agency to avoid such a situation.

Of course, there’s still the ‘knowledge should be free’ etc. argument, but this was to show that even if one were not to have that viewpoint, it’s still not a smart move to publish the textbook with the well-known academic publishers, even more so if the topic isn’t in the core undergraduate computer science curriculum.

Interestingly, after ‘publishing’ it on my website and listing it on OpenUCT and the Open Textbook Archive—I’m certainly not the only one who had done a market analysis or has certain political convictions—one colleague pointed me to the non-profit College Publications that aims to “break the monopoly that commercial publishers have” and another colleague pointed me to UCT press. I had contacted both, and the former responded. In the meantime, the book has been published by CP and is now also listed on Amazon for just $18 (about 16 euro) or some R250 for the paperback version—whilst the original pdf file is still freely available—or: you pay for production costs of the paperback, which has a slightly nicer layout and the errata I knew of at the time have been corrected.

I have noticed that some people don’t take the informal self publishing seriously—even below the so-called ‘vanity publishers’ like Lulu—notwithstanding the archives to cater for it, the financial take on the matter, the knowledge sharing argument, and the ‘textbooks for development’ in emerging economies angle of it. So, I guess no brownie points from them then and, on top of that, my publication record did, and does, take a hit. Yet, writing a book, as an activity, is a nice and rewarding change from just churning out more and more papers like a paper production machine, and I hope it will contribute to keeping the OE research area alive and lead to better ontologies in ontology-driven information systems. The textbook got its first two citations already, the feedback is mostly very positive, readers have shared it elsewhere (reddit, ungule.it, Open Libra, Ebooks directory, and other platforms), and I recently got some funding from the DOT4D project to improve the resources further (for things like another chapter, new exercises, some tools development to illuminate the theory, a proofreading contest, updating the slides for sharing, and such). So, overall, if I had to make the choice again now, I’d still do it again the way I did. Also, I hope more textbook authors will start seeing self-publishing, or else non-profit, as a good option. Last, the notion of open textbooks is gaining momentum, so you even could become a trendsetter and be fashionable 😉

On ‘open access’ CS conference proceedings

It perhaps sounds nice and doing-good-like, for the doe-eyed ones at least: publish computer science conference proceedings as open access so that anyone in the world can access the scientific advances for free. Yay. Free access to scientific materials is good for a multitude of reasons. There’s downside in the set-up in the way some try to push this now, though, which amounts to making people pay for what used to be, and still mostly is, for free already. I take issue with that. Instead of individualising a downside of open access by heaping more costs onto the individual researchers, the free flow of knowledge should be—and remain—a collectivised effort.

 

It is, and used to be, the case that most authors put the camera-ready-copy (CRC) on their respective homepages and/or institutional repositories, and it used to be typically even before the conference (e.g., mine are here). Putting the CRC on one’s website or in an openly accessible institutional repository seems to happen slightly less often now, even though it is legal to do so. I don’t know why. Even if it were not entirely legal, a collective disobedience is not something that the publishers easily can fight. It doesn’t help that Google indexes the publisher quicker than the academics’ webpages, so the CRCs on the authors’ pages don’t turn up immediately in the search results even whey the CRCs are online, but that would be a pathetic reason for not uploading the CRC. It’s a little extra effort to lookup an author’s website, but acceptable as long as the file is still online and freely available.

Besides the established hallelujah’s to principles of knowledge sharing, there’s since recently a drive at various computer science (CS) conferences to make sure the proceedings will be open access (OA). Like for OA journal papers in an OA or hybrid journal, someone’s going to have to pay for the ‘article processing charges’. The instances that I’ve seen close-up, put those costs for all papers of the proceedings in the conference budget and therewith increase the conference registration costs. Depending on 1) how good or bad the deal is that the organisers made, 2) how many people are expected to attend, and 3) how many papers will go in the volume, it hikes up the registration costs by some 50 euro. This is new money that the publishing house is making that they did not use to make before, and I’m pretty sure they wouldn’t offer an OA option if it were to result in them making less profit from the obscenely lucrative science publishing business.

So, who pays? Different universities have different funding schemes, as have different funders as to what they fund. For instance, there exist funds for contributing to OA journal article publishing (also at UCT, and Springer even has a list of OA funders in several countries), but that cannot be used in this case, for the OA costs are hidden in the conference registration fee. There are also conference travel funds, but they fund part of it or cap it to a maximum, and the more the whole thing costs, the greater the shortfall that one then will have to pay out of one’s own research fund or one’s own pocket.

A colleague (at another university) who’s pushing for the OA for CS conference proceedings said that his institution is paying for all the OA anyway, not him—he easily can have principles, as it doesn’t cost him anything anyway. Some academics have their universities pay for the conference proceedings access already anyway, as part of the subscription package; it’s typically the higher-ranking technical universities that have access. Those I spoke to, didn’t like the idea that now they’d have to pay for access in this way, for they already had ‘free’ (to them) access, as the registration fees come from their own research funds. For me, it is my own research funds as well, i.e., those funds that I have to scramble together through project proposal applications with their low acceptance rates. If I’d go to/have papers at, say, 5 such conferences per year (in the past several years, it was more like double that), that’s the same amount as paying a student/scientific programmer for almost a week and about a monthly salary for the lowest-paid in South Africa, or travel costs or accommodation for the national CS&IT conference (or both) or its registration fees. That is, with increased registration fees to cover the additional OA costs, at least one of my students or I would lose out on participating in even a local conference, or students would be less exposed to doing research and obtaining programming experience that helps them to get a better job or better chance at obtaining a scholarship for postgraduate studies. To name but a few trade-offs.

Effectively, the system has moved from “free access to the scientific literature anyway” (the online CRCs), to “free access plus losing money (i.e.: all that I could have done with it) in the process”. That’s not an improvement on the ground.

Further, my hard-earned research funds are mine, and I’d like to decide what to do with it, rather than having that decision been taken for me. Who do the rich boys up North think they are to say that I should spend it on OA when the papers were already free, rather than giving a student an opportunity to go to a national conference or devise and implement an algorithm, or participate in an experiment etc.! (Setting aside them trying to reprimand and ‘educate’ me on the goodness—tsk! as if I don’t know that the free flow of scientific information is a good thing.)

Tell me, why should the OA principles trump the capacity building when the papers are free access already anyway? I’ve not seen OA advocates actually weighing up any alternatives on what would be the better good to spend money on. As to possible answers, note that an “it ought to be the case that there would be enough money for both” is not a valid answer in discussing trade-offs, nor is a “we might add a bit of patching up as conference registration reduction for those needy that are not in the rich inner core” for it hardly ever happens, nor is a “it’s not much for each instance, you really should be able to cover it” because many instances do add up. We all know that funding for universities and for research in general is being squeezed left, right, and centre in most countries, especially over the past 8-10 years, and such choices will have to, and are being, made already. These are not just choices we face in Africa, but this holds also in richer countries, like in the EU (fewer resources in relative or absolute terms and greater divides), although a 250 euro (the 5 conferences scenario) won’t go as far there as in low-income countries.

Also, and regardless the funding squeeze: why should we start paying for free access that already was a de facto, and with most CS proceedings publishers, also a de jure, free access anyway? I’m seriously starting to wonder who’s getting kickbacks for promoting and pushing this sort of scheme. It’s certainly not me, and nor would I take it if some publisher would offer it to me, as it contributes to the flow of even more money from universities and research institutes to the profits of multinationals. If it’s not kickbacks, then to all those new ‘conference proceedings need to be OA’ advocates: why do you advocate paying for a right that we had for free? Why isn’t it enough for you to just pay for a principle yourself as you so desire, but instead insist to force others to do so too even when there is already a tacit and functioning agreement going on that realises that aim of free flow of knowledge?

Sure, the publisher has a responsibility to keep the papers available in perpetuity, which I don’t, and link rot does exist. One easily could write a script to search all academics’ websites and get the files, like citeseer used to do well. They get funding for such projects for long-term archiving, like arxiv.org does as well, and philpapers, and SSRN as popular ones (see also a comprehensive list of preprint servers), and most institution’s repositories, too (e.g., the CS@UCT pubs repository). So, the perpetuity argument can also be taken care of that way, without the researchers actually having to pay more.

Really, if you’re swimming in so much research money that you want to pay for a principle that was realised without costs to researchers, then perhaps instead do fund the event so that, say, some student grants can be given out, that it can contribute to some nice networking activity, or whatever part of the costs. The new “we should pay for OA, notwithstanding that no one was suffering when it was for free” attitude for CS conference proceedings is way too fishy to actually being honest; if you’re honest and not getting kickbacks, then it’s a very dumb thing to advocate for.

For the two events where this scheme is happening that I’m involved in, I admit I didn’t forcefully object at the time it was mentioned (nor had I really thought through the consequences). I should have, though. I will do so a next time.

Gastrophysics and follies

Yes, turns out there is a science of eating, which is called gastrophysics, and a popular science introduction to the emerging field was published in an accessible book this year by Charles Spence (Professor [!] Charles Spence, as the front cover says), called, unsurprisingly, Gastrophysics—the new science of eating. The ‘follies’ I added to the blog post title refers to the non-science parts of the book, which is a polite term that makes it a nice alliteration in the pronunciation of the post’s title. The first part of this post is about the interesting content of the book; the second part about certain downsides.

The good and interesting chapters

Given that some people don’t even believe there’s a science to food (there is, a lot!), it is perhaps even a step beyond to contemplate there can be such thing as a science for the act of eating and drinking itself. Turns out—quite convincingly in the first couple of chapters of the book—that there’s more to eating than meets the eye. Or taste bud. Or touch. Or nose. Or ear. Yes, the ear is involved too: e.g., there’s a crispy or crunchy sound when eating, say, crisps or corn flakes, and it is perceived as an indicator of the freshness of the crisps/cornflakes. When it doesn’t crunch as well, the ratings are lower, for there’s the impression of staleness or limpness to it. The nose plays two parts: smelling the aroma before eating (olfactory) and when swallowing as volatile compounds are released in your throat that reach your nose from the back when breathing out (i.e., retronasal).

The first five chapters of the books are the best, covering taste, smell, sight, sound, and touch. They present easily readable interesting information that is based on published scientific experiments. Like that drinking with a straw ruins the smell-component of the liquid (and so does drinking from a bottle) cf drinking from a glass that sets the aromas free to combine the smell with the taste for a better overall evaluation of the drink. Or take the odd (?) thing that frozen strawberry dessert tastes sweeter from a white bowl than a black one, as is eating it from a round plate cf. from an angular plate. Turns out there’s some neuroscience to shapes (and labels) that may explain the latter. If you think touch and cutlery don’t matter: it’s been investigated, and it does. The heavy cutlery makes the food taste better. It’s surface matters, too. The mouth feel isn’t the same when eating with a plain spoon vs. from a spoon that was first dipped in lemon juice and then in sugar or ground coffee (let it dry first).

There is indeed, as the intro says, some fun fact on each of these pages. It is easy to see that these insights also can be interesting to play with for one’s dinner as well as being useful to the food industry, and to food science, be it to figure out the chemistry behind it or how to change the product, the production process, or even just the packaging. Some companies did so already. Like when you open a bag of (relatively cheap-ish) ground coffee: the smell is great, but that’s only because some extra aroma was added in the sealed air when it was packaged. Re-open the container (assuming you’ve transferred it into one), and the same coffee smell does not greet you anymore. The beat of the background music apparently also affects the speed of masticating. Of course, the basics of this sort of stuff were already known decades ago. For instance, the smell of fresh bread in the supermarket is most likely aroma in the airco, not the actual baking all the time when the shop is open (shown to increase buying bread, if not more), and the beat of the music in the supermarket affects your walking speed.

On those downsides of the book

After these chapters, it gradually goes downhill with the book’s contents (not necessarily the topics). There are still a few interesting science-y things to be learned from the research into airline food. For instance, that the overall ‘experience’ is different because of lower humidity (among other things) so your nose dries out and thus detects less aroma. They throw more sauce and more aromatic components into the food being served up in the air. However, the rest descends into a bunch of anecdotes and blabla about fancy restaurants, with the sources not being solid scientific outlets anymore, but mostly shoddy newspaper articles. Yes, I’m one of those who checks the footnotes (annoyingly as endnotes, but one can’t blame the author for that sort of publisher’s mistake). Worse, it gives the impression of being research-based, because it was so in the preceding chapters. Don’t be fooled by the notes in, especially, chapters 9-12. To give an example, there’s a cool-sounding section on “do robot cooks make good chefs?” in the ‘digital dining’ chapter. One expects an answer; but no, forget that. There’s some hyperbole with the author’s unfounded opinion and, to top it off, a derogatory remark about his wife probably getting excited about a 50K GBP kitchen gadget. Another example out of very many of this type: some opinion by some journalist who ate some day, in casu at über-fancy way-too-expensive-for-the-general-reader Pairet’s Ultraviolet (note 25 on p207). Daily Telegraph, New York Times, Independent, BBC, Condiment junkie, Daily Mail Online, more Daily Mail, BBC, FT Weekend Magazine, Wired, Newsweek etc. etc. Come on! Seriously?! It is supposed to be a popsci book, so then please don’t waste my time with useless anecdotes and gut-feeling opinions without (easily digestible) scientific explanations. Or they should have split the book in two: I) popsci and II) skippable waffle that any science editor ought not to have permitted to pass the popsci book writing and publication process. Professor Spence is encouraged to reflect a little on having gone down on a slippery slope a bit too much.

In closing

Although I couldn’t bear to finish reading the ‘experiential meal’ chapter, I did read the rest, and the final chapter. As any good meal that has to have a good start and finish, the final chapter is fine, including the closing [almost] with the Italian Futurists of the 1930s (or: weird dishes aren’t so novel after all). As to the suggestions for creating your own futurist dinner party, I can’t withhold here the final part of the list:

In conclusion: the book is worth reading, especially the first part. Cooking up a few experiments of my own sounds like a nice pastime.

Conjuring up or enhancing a new subdiscipline, say, gastromatics, computational gastronomy, or digital gastronomy could be fun. The first term is a bit too close to gastromatic (the first search hits are about personnel management software in catering), though, and the second one has been appropriated by the data mining and Big Data crowd already. Digital gastronomy has been coined as well and seems more inclusive on the technology side than the other two. If it all sounds far-fetched, here’s a small sampling: there are already computer cooking contests (at the case-based reasoning conferences) for coming up with the best recipe given certain constraints, a computational analysis of culinary evolution, data mining in food science and food pairing in Arab cuisine, robot cocktail makers are for sale (e.g., makr shakr and barbotics) and there’s also been research on robot baristas (e.g., the FusionBot and lots more), and more, much more, results over at least the past 10 years.

Robot peppers, monkey gland sauce, and go well—Say again? reviewed

The previous post about TDDonto2 had as toy example a pool braai, which does exist in South Africa at least, but perhaps also elsewhere under a different name: the braai is the ‘South African English’ (SAE) for the barbecue. There are more such words and phrases peculiar to SAE, and after the paper deadline last week, I did finish reading the book Say again? The other side of South African English by Jean Branford and Malcolm Venter (published earlier this year) that has many more examples of SAE and a bit of sociolinguistics and some etymology of that. Anyone visiting South Africa will encounter at least several of the words and sentence constructions that are SAE, but probably would raise eyebrows elsewhere. Let me start with some examples.

Besides the braai, one certainly will encounter the robot, which is a traffic light (automating the human police officer). A minor extension to that term can be found in the supermarket (see figure on the right): robot peppers, being a bag of three peppers in the colours of red, yellow, and green—no vegetable AI, sorry.

How familiar the other ones discussed in the book are, depends on how much you interact with South Africans, where you stay(ed), and how much you read and knew about the country before visiting it, I suppose. For instance, when I visited Pretoria in 2008, I had not come across the bunny, but did so upon my first visit in Durban in 2010 (it’s a hollowed-out half a loaf of bread, filled with a curry) and bush college upon starting to work at a university (UKZN) here in 2011. The latter is a derogatory term that was used for universities for non-white students in the Apartheid era, with the non-white being its own loaded term from the same regime. (It’s better not to use it—all terms for classifying people one way or another are a bit of a mine field, whose nuances I’m still trying to figure out; the book didn’t help with that).

Then there’s the category of words one may know from ‘general English’, but are by the authors claimed to have a different meaning here. One is the sell-outs, which is “to apply particularly to black people who were thought to have betrayed their people” (p143), though I have the impression it can be applied generally. Another is townhouse, which supposedly has narrowed its meaning cf. British English (p155), but from having lived on the isles some years ago, it was used in the very same way as it is here; the book’s authors just stick to its older meaning and assume the British and Irish do so too (they don’t, though). One that indeed does fall in the category ‘meaning restriction’ is transformation (an explanation of the narrower sense will take up too much space). While I’ve learned a bunch of the ‘unusual’ usual words in the meantime I’ve worked here, there were others that I still did wonder about. For instance, the lay-bye, which the book explained to be the situation when the shop sets aside a product the customer wants, and the customer pays the price in instalments until it is fully paid before taking the product home. The monkey gland sauce one can buy in the supermarket is another, which is a sauce based on ketchup and onion with some chutney in it—no monkeys and no glands—but, I’ll readily admit, I still have not tried it due to its awful name.

There are many more terms described and discussed in the book, and it has a useful index at the end, especially given that it gives the impression to be a very popsci-like book. The content is very nicely typesetted, with news item snippets and aside-boxes and such. Overall, though, while it’s ok to read in the gym on the bicycle for a foreigner who sometimes wonders about certain terms and constructions, it is rather uni-dimensional from a British White South African perspective and the authors are clearly Cape Town-based, with the majority of examples from SA media from Cape Town’s news outlets. They take a heavily Afrikaans-influence-only bias, with, iirc, only four examples of the influence of, e.g., isiZulu on SAE (e.g., the ‘go well’ literal translation of isiZulu’s hamba kahle), which is a missed opportunity. A quick online search reveals quite a list of words from indigenous languages that have been adopted (and more here and here and here and here) such as muti (medicine; from the isiZulu umuthi) and maas (thick sour milk; from the isiZulu amasi) and dagga (marijuana; from the Khoe daxa-b), not to mention the many loan words, such as indaba (conference; isiZulu) and ubuntu (the concept, not the operating system—which the authors seem to be a bit short of, given the near blind spot on import of words with a local origin). If that does not make you hesitant to read it, then let me illustrate some more inaccuracies beyond the aforementioned townhouse squabble, which results in having to take the book’s contents probably with a grain of salt and heavily contextualise it, and/or at least fact-check it yourself. They fall in at least three categories: vocabulary, grammar, and etymology.

To quote: “This came about because the Dutch term tijger means either tiger or leopard” (p219): no, we do have a word for leopard: luipaard. That word is included even in a pocket-size Prisma English-Dutch dictionary or any online EN-NL dictionary, so a simple look-up to fact-check would have sufficed (and it existed already in Dutch before a bunch of them started colonising South Africa in 1652; originating from old French in ~1200). Not having done so smells of either sloppiness or arrogance. And I’m not so sure about the widespread use of pavement special (stray or mongrel dogs or cats), as my backyard neighbours use just stray for ‘my’ stray cat (whom they want to sterilise because he meows in the morning). It is a fun term, though.

Then there’s stunted etymology of words. The coconut is not a term that emerged in the “new South Africa” (pp145-146), but is transferred from the Americas where it was already in use for at least since the 1970s to denote the same concept (in short: a brown skinned person who is White on the inside) but then applied to some people from Central and South America [Latino/Hispanic; take your pick].

Extending the criticism also to the grammar explanations, the “with” aside box on pp203-204 is wrong as well, though perhaps not as blatantly obvious as the leopard and coconut ones. The authors stipulate that phrases like “Is So-and-So coming with?” (p203) is Afrikaans influence of kom saam “where saam sounds like ‘with’” (p203) (uh, no, it doesn’t), and as more guessing they drag a bit of German influence in US English into it. This use, and the related examples like the “…I have to take all my food with” (p204) is the same construction and similar word order for the Dutch adverb mee ‘with’ (and German mit), such as in the infinitives meekomen ‘to come with’ (komen = to come), meenemen ‘to take with’, meebrengen ‘to bring with’, and meegaan ‘to go with’. In a sentence, the mee may be separated from the rest of the verb and put somewhere, including at the end of the sentence, like in ik neem mijn eten mee ‘I take my food with’ (word-by-word translated) en komt d’n dieje mee? ‘comes so-and-so with?’ (word-by-word translated, with a bit of ABB in the Dutch). German has similar infinitives—mitkommen, mitnehmen, mitbringen, and mitgehen, respectively—sure, but the grammar construction the book’s authors highlight is so much more likely to come from Dutch as first step of tracing it back, given that Afrikaans is a ‘simplified’ version of Dutch, not of German. (My guess would be that the Dutch mee- can be traced back, in turn, to the German mit, as Dutch is a sort of ‘simplified’ German, but that’s a separate story.)

In closing, I could go on with examples and corrections, and maybe I should, but I think I made the point clear. The book didn’t read as badly as it may seem from this review, but writing the review required me to fact-check a few things, rather than taking most of it at face value, which made it turn out more and more mediocre than the couple of irritations I had whilst reading it.

Reblogging 2010: South African women on leadership in science, technology and innovation

From the “10 years of keetblog – reblogging: 2010”: while the post’s data are from 5 years ago, there’s still room for improvement. That said, it’s not nearly as bad as in some other countries, like the Netherlands (though the university near my home town improved from 1.6% to 5% women professors over the past 5 years). As for the places I worked post-PhD, the percent female academics with full time permanent contract: FUB-KRDB group 0% (still now), UKZN-CS-Westville: 12.5% (me; 0% now), UCT-CS: 42%.

South African Women on leadership in science, technology and innovation; August 13, 2010

 

Today I participated in the Annual NACI symposium on the leadership roles of women in science, technology and innovation in Pretoria, which was organized by the National Advisory Council on Innovation, which I will report on further below. As preparation for the symposium, I searched a bit to consult the latest statistics and see if there are any ‘hot topics’ or ‘new approaches’ to improve the situation.

General statistics and their (limited) analyses

The Netherlands used to be at the bottom end of the country league tables on women professors (from my time as elected representative in the university council at Wageningen University, I remember a UN table from ’94 or ‘95 where the Netherlands was third last from all countries). It has not improved much over the years. From Myklebust’s news item [1], I sourced the statistics to Monitor Women Professors 2009 [2] (carried out by SoFoKleS, the Dutch social fund for the knowledge sector): less than 12% of the full professors in the Netherlands are women, with the Universities of Leiden, Amsterdam, and Nijmegen leading the national league table and the testosterone bastion Eindhoven University of Technology closing the ranks with a mere 1.6% (2 out of 127 professors are women). With the baby boom generation lingering on clogging the pipeline since a while, the average percentage increase has been about 0.5% a year—way too low to come even near the EU Lisbon Agreement Recommendation’s target of 25% by 2010, or even the Dutch target of 15%, but this large cohort will retire soon, and, in terms of the report authors, makes for a golden opportunity to move toward gender equality more quickly. The report also has come up with a “Glass Ceiling Index” (GCI, the percentage of women in job category X-1 divided by the percentage of women in job category X) and, implicitly, an “elevator” index for men in academia. In addition to the hard data to back up the claim that the pipeline is leaking at all stages, they note it varies greatly across disciplines (see Table 6.3 of the report): in science, the most severe blockage is from PhD to assistant professor, in Agriculture, Technology, Economics, and Social Sciences it is the step from assistant to associate professor, and for Law, Language & Culture, and ‘miscellaneous’, the biggest hurdle is from associate to full professor. From all GCIs, the highest GCI (2.7) is in Technology in the promotion from assistant to associate professor, whereas there is almost parity at that stage in Language & Culture (GCI of 1.1, the lowest value anywhere in Table 6.3).

“When you’re left out of the club, you know it. When you’re in the club, you don’t see what the problem is.” Prof. Jacqui True, University of Auckland [4]

Elsewhere in ‘the West’, statistics can look better (see, e.g., The American Association of University Professors (AAUP) survey on women 2004-05), or are not great either (UK, see [3], but the numbers are a bit outdated). However, one can wonder about the meaning of such statistics. Take, for instance, the NYT article on a poll about paper rights vs. realities carried out by The Pew Research in 22 countries [4]: in France, some 100% paid their lip service to being in favour of equal rights, yet 75% also said that men had a better life. It is only in Mexico (56%), Indonesia (55%) and Russia (52%) that the people who were surveyed said that women and men have achieved a comparable quality of life. But note that the latter statement is not the same as gender equality. And equal rights and opportunities by law does not magically automatically imply the operational structures are non-discriminatory and an adequate reflection of the composition of society.

A table that has generated much attention and questions over the years—but, as far as I know, no conclusive answers—is the one published in Science Magazine [5] (see figure below). Why is it the case that there are relatively much more women physics professors in countries like Hungary, Portugal, the Philippines and Italy than in, say, Japan, USA, UK, and Germany? Recent guessing for the answer (see blog comments) are as varied as the anecdotes mentioned in the paper.

Physics professors in several countries (Source: 5).

Barinaga’s [5] collection of anecdotes of several influential factors across cultures include: a country’s level of economic development (longer established science propagates the highly patriarchal society of previous centuries), the status of science there (e.g., low and ‘therefore’ open to women), class structure (pecking order: rich men, rich women, poor men, poor women vs. gender structure rich men, poor men, rich women, poor women), educational system (science and mathematics compulsory subjects at school, all-girls schools), and the presence or absence of support systems for combining work and family life (integrated society and/or socialist vs. ‘Protestant work ethic’), but the anecdotes “cannot purport to support any particular conclusion or viewpoint”. It also notes that “Social attitudes and policies toward child care, flexible work schedules, and the role of men in families dramatically color women’s experiences in science”. More details on statistics of women in science in Latin America can be found in [6] and [7], which look a lot better than those of Europe.

Barbie the computer engineer

Bonder, in her analysis for Latin America [7], has an interesting table (cuadro 4) on the changing landscape for trying to improve the situation: data is one thing, but how to struggle, which approaches, advertisements, and policies have been, can, or should be used to increase women participation in science and technology? Her list is certainly more enlightening than the lame “We need more TV shows with women forensic and other scientists. We need female doctor and scientist dolls.” (says Lotte Bailyn, a professor at MIT) or “Across the developed world, academia and industry are trying, together or individually, to lure women into technical professions with mentoring programs, science camps and child care.” [8] that only very partially addresses the issues described in [5]. Bonder notes shifts in approaches from focusing only on women/girls to both sexes, from change in attitude to change in structure, from change of women (taking men as the norm) to change in power structures, from focusing on formal opportunities to targeting to change the real opportunities in discriminatory structures, from making visible non-traditional role models to making visible the values, interests, and perspectives of women, and from the simplistic gender dimension to the broader articulation of gender with race, class, and ethnicity.

The NACI symposium

The organizers of the Annual NACI symposium on the leadership roles of women in science, technology and innovation provided several flyers and booklets with data about women and men in academia and industry, so let us start with those. Page 24 of Facing the facts: Women’s participation in Science, Engineering and Technology [9] shows the figures for women by occupation: 19% full professor, 30% associate professor, 40% senior lecturer, 51% lecturer, and 56% junior lecturer, which are in a race distribution of 19% African, 7% Coloured, 4% Indian, and 70% White. The high percentage of women participation (compared to, say, the Netherlands, as mentioned above) is somewhat overshadowed by the statistics on research output among South African women (p29, p31): female publishing scientists are just over 30% and women contributed only 25% of all article outputs. That low percentage clearly has to do with the lopsided distribution of women on the lower end of the scale, with many junior lecturers who conduct much less research because they have a disproportionate heavy teaching load (a recurring topic during the breakout session). Concerning distribution of grant holders in 2005, in the Natural & agricultural sciences, about 24% of the total grants (211 out of 872) have been awarded to women and in engineering & technology it is 11% (24 out of 209 grants) (p38). However, in Natural & agricultural sciences, women make up 19% and in engineering and technology 3%, which, taken together with the grant percentages, show there is a disproportionate amount of women obtaining grants in recent years. This leads one to suggest that the ones that actually do make it to the advanced research stage are at least equally as good, if not better, than their male counterparts. Last year, women researchers (PIs) received more than half of the grants and more than half of the available funds (table in the ppt presentation of Maharaj, which will be made available online soon).

Mrs Naledi Pandor, the Minister for Science and Technology, held the opening speech of the event, which was a good and entertaining presentation. She talked about the lack of qualified PhD supervisors to open more PhD positions, where the latter is desired so as to move to the post-industrial, knowledge-based economy, which, in theory at least, should make it easier for women to participate than in an industrial economy. She also mentioned that one should not look at just the numbers, but instead at the institutional landscape so as to increase opportunities for women. Last, she summarized the “principles and good practice guidelines for enhancing the participation of women in the SET sector”, which are threefold: (1) sectoral policy guidelines, such as gender mainstreaming, transparent recruiting policies, and health and safety at the workplace, (2) workplace guidelines, such as flexible working arrangements, remuneration equality, mentoring, and improving communication lines, and (3) re-entry into the Science, Engineering and Technology (SET) environment, such as catch-up courses, financing fellowships, and remaining in contact during a career break.

Dr. Thema, former director of international cooperation at the Department of Science and Technology added the issues of the excessive focus on administrative practicalities, the apartheid legacy and frozen demographics, and noted that where there is no women’s empowerment, this is in violation of the constitution. My apologies if I have written her name and details wrongly: she was a last-minute replacement for Prof. Immaculada Garcia Fernández, department of computer science at the University of Malaga, Spain. Garcia Fernández did make available her slides, which focused on international perspectives on women leadership in STI. Among many points, she notes that the working conditions for researchers “should aim to provide… both women and men researchers to combine work and family, children and career” and “Particular attention should be paid, to flexible working hours, part-time working, tele-working and sabbatical leave, as well as to the necessary financial and administrative provisions governing such arrangements”. She poses the question “The choice between family and profession, is that a gender issue?”

Dr. Romilla Maharaj, executive director for human and institutional capacity development at the National Research Foundation came with much data from the same booklet I mentioned in the first paragraph, but little qualitative analysis of this data (there is some qualitative information). She wants to move from the notion of “incentives” for women to “compensation”. The aim is to increase the number of PhDs five-fold by 2018 (currently the rate is about 1200 each year), which is not going to be easy (recollect the comment by the Minister, above). Concerning policies targeted at women participation, they appear to be successful for white women only (in postdoc bursaries, white women even outnumber white men). In my opinion, this smells more of a class/race structure issue than a gender issue, as mentioned above and in [5]. Last, the focus of improvements, according to Maharaj, should be on institutional improvements. However, during the break-out session in the afternoon, which she chaired, she seemed to be selectively deaf on this issue. The problem statement for the discussion was the low research output by women scientists compared to men, and how to resolve that. Many participants reiterated the lack of research time due to the disproportionate heavy teaching load (compared to men) and what is known as ‘death by committee’, and the disproportionate amount of (junior) lecturers who are counted in the statistics as scientists but, in praxis, do not do (or very little) research, thereby pulling down the overall statistics for women’s research output. Another participant wanted to se a further breakdown of the numbers by age group, as the suspicion was that it is old white men who produce most papers (who teach less, have more funds, supervise more PhD students etc.) (UPDATE 13-10-’10: I found some data that seems to support this). In addition, someone pointed out that counting publications is one thing, but considering their impact (by citations) is another one and for which no data was available, so that a recommendation was made to investigate this further as well (and to set up a gender research institute, which apparently does not yet exist in South Africa). The pay-per-publication scheme implemented at some universities could thus backfire for women (who require the time and funds to do research in the first place so as to get at least a chance to publish good papers). Maharaj’s own summary of the break-out session was an “I see, you want more funds”, but that does not rhyme fully with he institutional change she mentioned earlier nor with the multi-faceted problems raised during the break-out session that did reveal institutional hurdles.

Prof. Catherine Odora Hoppers, DST/NRF South African Research Chair in Development Education (among many things), gave an excellent speech with provoking statements (or: calling a spade a spade). She noted that going into SET means entering an arena of bad practice and intolerance; to fix that, one first has to understand how bad culture reproduces itself. The problem is not the access, she said, but the terms and conditions. In addition, and as several other speakers already had alluded to as well, she noted that one has to deal with the ghosts of the past. She put this in a wider context of the history of science with the value system it propagates (Francis Bacon, my one-line summary of the lengthy quote: science as a means to conquer nature so that man can master and control it), and the ethics of SET: SET outcomes have, and have had, some dark results, where she used the examples of the atom bomb, gas chambers, how SET was abused by the white male belittling the native and that it has been used against the majority of people in South Africa, and climate change. She sees the need for a “broader SET”, meaning ethical, and, (in my shorthand notation) with social responsibility and sustainability as essential components. She is putting this into practice by stimulating transdisciplinary research at her research group, and, at least and as a first step: people from different disciplines must to be able to talk to each other and understand each other.

To me, as an outsider, it was very interesting to hear what the current state of affairs is regarding women in SET in South Africa. While there were complaints, there we also suggestions for solutions, and it was clear from the data available that some improvements have been made over the years, albeit only in certain pockets. More people registered for the symposium than places available, and with some 120 attendees from academia and industry at all stages of the respective career paths, it was a stimulating mix of input that I hope will further improve the situation on the ground.

References

[1] Jan Petter Myklebust. THE NETHERLANDS: Too few women are professors. University World News, 17 January 2010, Issue: 107.

[2] Marinel Gerritsen, Thea Verdonk, and Akke Visser. Monitor Women Professors 2009. SoFoKleS, September 2009.

[3] Helen Hague. 9.2% of professors are women. Times Higher Education, May 28, 1999.

[4] Victoria Shannon. Equal rights for women? Surveys says: yes, but…. New York Times/International Herald Tribune—The female factor, June 30, 2010.

[5] Marcia Barinaga. Overview: Surprises Across the Cultural Divide. Compiled in: Comparisons across cultures. Women in science 1994. Science, 11 March 1994 263: 1467-1496 [DOI: 10.1126/science.8128232]

[6] Beverley A. Carlson. Mujeres en la estadística: la profesión habla. Red de Reestructuración y Competitividad, CEPAL – SERIE Desarrollo productivo, nr 89. Santiago de Chile, Noviembre 2000.

[7] Gloria Bonder. Mujer y Educación en América Latina: hacia la igualdad de oportunidades. Revista Iberoamericana de Educación, Número 6: Género y Educación, Septiembre – Diciembre 1994.

[8] Katrin Benhold. Risk and Opportunity for Women in 21st Century. New York Times International Herald Tribune—The female factor, March 5, 2010.

[9] Anon. Facing the facts: Women’s participation in Science, Engineering and Technology. National Advisory Council on Innovation, August 2009.

Reblogging 2008: Failing to recognize your own incompetence

From the “10 years of keetblog – reblogging: 2008”: On those uncomfortable truths on the difference between knowing what you don’t know and not knowing what you don’t know… (and one of the earlier Ig Nobel prize winners 15 years ago)

Failing to recognize your own incompetence; Aug 25, 2008

 

Somehow, each time when I mention to people the intriguing 2000 Ig Nobel prize winning paper “Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments” [1], they tend to send (non)verbal signals demonstrating a certain discomfort. Then I tone it down a bit, saying that one could argue about the set up of the experiment that led Kruger & Dunning to their conclusion. Now—well, based on material from a few years ago but I found out recently—I cannot honestly say that anymore either. A paper from the same authors, “Why people fail to recognize their own incompetence” [2], reports not only more of their experiments in different settings, but also different experiments by other researchers validating the basic tenet that ignorant and incompetent people do not realize they are incompetent but rather think more favourably of themselves—“tend to hold overinflated views of their skills”—than can be justified based on their performance.

Yeah, the shoe might fit. Or not. In addition to the lower end of the scale overestimating their competencies by a large margin, the converse happens, though to a lesser extent, at the other end of the scale, where top-experts underestimate their actual capabilities. The latter brings it own set of problems and research directions, which I will set aside for the remainder of this blog post. Instead, I will dwell a bit on those people bragging to know this that and the other, but, alas, do not perform properly and, moreover, do not even realize they do not. Facing a person who knows s/he does not have the required skills is one thing and generally s/he’s willing to listen and learn or say to not care about it, but those people who do not realize the knowledge/skills gap they have are, well, a hopeless bunch futile to waste your time on (unless you teach them anyway).

 Let us have a look what those psychologists provided to come to this conclusion. Aside from the experiment about jokes in the ’99 paper, which are at least (sub)culture-dependent, the data about the introductory-level psychology class taken by 141 students is quite telling. Right after the psych exam, the students were asked about their own estimate of performance & mastery of the course material (relative to other students in their class) and to estimate their raw score of the exam. These were the results ([2] p84, Fig.1):

If you think such kind of data is only observed with undergraduates in psychology, well, then check [2]’s references: debate teams, hunters about their firearms, medical residents (over)estimating their patient-interviewing techniques, medical lab technicians overestimating their knowledge of medical terminology—you name it, the same pattern, even if the subjects were held a carrot of monetary incentive in an attempt to assess themselves honestly.

 Imagine you going to a GP or doctor of a regional hospital who has the arrogance to know it all and does not call in a specialist on time. One can debate about the harmfulness or harmlessness about such cases. A very recent incident I observed was where x1 and x2 demanded from y to do nonsensical task z. Task z—exemplifying ignorance and incompetence of x1 and x2—was not carried out by y for it could not be done, but it was nevertheless used by x1 and x2 to “demonstrate” “(inherent) incompetence” of y because y did not do task z, whereas, in fact, it the only thing it shows is that y, unlike x1 and x2, may actually have realized z could not be done, hence, understand z better than x1 and x2 do. One’s incompetence [in this case, of x1 and x2] can have far-reaching effects on others around oneself. Trying to get x1 and x2 to realize their shortcomings has not worked thus far. Dunning et al’s students, however, had exam results for unequivocal feedback and there was an additional test set up with a controlled setting where they had built-in a lecture to teach the incompetent so as to rate their competencies better (which worked to some extent), but in real life those options are not always available. What options are available, if any? A prevalent approach I observed here in Italy (well, in academia at least) is that Italians tend to ignore those xs so as to limit as much as possible the ‘air time’ and attention they have, i.e., an avoidance strategy to leave the incompetent be, whereas, e.g., in the Netherlands people will tend to keep on talking until they have blisters on their tongues (figuratively) to try to get some sense in the xs heads, and yet others attempt to sweep things under the carpet and pray there will not appear any wobbles one could fall over. Research directions, let alone some practical suggestions on “how to let people become aware of their intellectual and social deficiencies”—other than ‘teach them’—were not mentioned in the article, but made it to the list of future works.

 You might wonder: does this hold across cultures? The why of the ‘ignorant and unaware of it’ gives some clues that, in theory, culture may not have anything to do with it.

“In many intellectual and social domains, the skills needed to produce correct responses are virtually identical to those needed to evaluate the accuracy of one’s responses… Thus, if people lack the skills to produce correct answers, they are also cursed with an inability to know when their, or anyone else’s, are right or wrong. They cannot recognize their responses as mistaken, or other people’s responses as superior to their own.” ([2], p. 86—emphasis added)

The principal problem has to do with so-called meta-cognition, which “refers to the ability to evaluate responses as correct or incorrect”, and incompetence then entails that one cannot successfully complete such a task; this is a catch-22, but, as mentioned, ‘outside intervention’ through teaching appeared to work and other means are a topic of further investigation. Clearly, a culture of arrogance can make significant stats more significant, but it does not change the principle of the cause. In this respect, the start of the article aptly quotes Confucius: “Real knowledge is to know the extent of one’s ignorance”. Conversely, according to Whitehead (quoted on p. 86 of [2]), “it is not ignorance, but ignorance of ignorance, that is the death of knowledge”.

References

[1] Kruger, J., Dunning, D. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of personality and Social Psychology, 1999, 77: 1121-1134.

[2] Dunning, D., Johnson, K., Ehrlinger, J., Kruger, J. Why people fail to recognize their own incompetence. Current Directions in Psychological Science, 2003, 12(3): 83-87.

 p.s.: I am aware of the fact that I do not know much about psychology, so my rendering, interpretation, and usage of the content of those papers may well be inaccurate, although I fancy the thought that I understood them.

Reblogging 2007: AI and cultural heritage workshop at AI*IA’07

From the “10 years of keetblog – reblogging: 2007”: a happy serendipity moment when I stumbled into the AI & Cultural heritage workshop, which had its presentations in Italian. Besides the nice realisation I actually could understand most of it, I learned a lot about applications of AI to something really useful for society, like the robot-guide in a botanical garden, retracing the silk route, virtual Rome in the time of the Romans, and more.

AI and cultural heritage workshop at AI*IA’07, originally posted on Sept 11, 2007. For more recent content on AI & cultural heritage, see e.g., the workshop’s programme of 2014 (also collocated with AI*IA).

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I’m reporting live from the Italian conference on artificial intelligence (AI*IA’07) in Rome (well, Villa Mondrogone in Frascati, with a view on Rome). My own paper on abstractions is rather distant from near-immediate applicability in daily life, so I’ll leave that be and instead write about an entertaining co-located workshop about applying AI technologies for the benefit of cultural heritage that, e.g., improve tourists’ experience and satisfaction when visiting the many historical sites, museums, and buildings that are all over Italy (and abroad).

I can remember well the handheld guide at the Alhambra back in 2001, which had a story by Mr. Irving at each point of interest, but there was only one long story and the same one for every visitor. Current research in AI & cultural heritage looks into solving issues how this can be personalized and be more interactive. Several directions are being investigated how this can be done. This ranges from the amount of information provided at each point of interest (e.g., for the art buff, casual American visitor who ‘does’ a city in a day or two, or narratives for children), to location-aware information display (the device will detect which point of interest you are closest to), to cataloguing and structuring the vast amount of archeological information, to the software monitoring of Oetzi the Iceman. The remainder of this blog post describes some of the many behind-the-scenes AI technologies that aim to give a tourist the desired amount of relevant information at the right time and right place (see the workshop website for the list of accepted papers). I’ll add more links later; any misunderstandings are mine (the workshop was held in Italian).

First something that relates somewhat to bioinformatics/ecoinformatics: the RoBotanic [1], which is a robot guide for botanical gardens – not intended to replace a human, but as an add-on that appeals in particular to young visitors and get them interested in botany and plant taxonomy. The technology is based on the successful ciceRobot that has been tested in the Archeological Museum Agrigento, but having to operate outside in a botanical garden (in Palermo), new issues have to be resolved, such as tuff powder, irregular surface, lighting, and leaves that interfere with the GPS system (for the robot to stop at plants of most interest). Currently, the RoBotanic provides one-way information, but in the near-future interaction will be built in so that visitors can ask questions as well (ciceRobot is already interactive). Both the RoBotanic and ciceRobot are customized off-the shelf robots.

Continuing with the artificial, there were three presentations about virtual reality. VR can be a valuable add-on to visualize lost or severely damaged property, timeline visualizations of rebuilding over old ruins (building a church over a mosque or vice versa was not uncommon), to prepare future restorations, and general reconstruction of the environment, all based on the real archeological information (not Hollywood fantasy and screenwriting). The first presentation [2] explained how the virtual reality tour of the Church of Santo Stefano in Bologna was made, using Creator, Vega, and many digital photos that served for the texture-feel in the VR tour. [3] provided technical details and software customization for VR & cultural heritage. On the other hand, the third presentation [4] was from a scientific point most interesting and too full of information to cover it all here. E. Bonini et al. investigated if, and if yes how, VR can give added-value. Current VR being insufficient for the cultural heritage domain, they look at how one can do an “expansion of reality” to give the user a “sense of space”. MUDing on the via Flaminia Antica in the virtual room in the National Museum in Rome should be possible soon (CNR-ITABC project started). Another issue came up during the concluded Appia Antica project for Roman era landscape VR: behaviour of, e.g., animals are now pre-coded and become boring to the user quickly. So, what these VR developers would like to see (i.e., future work) is to have technologies for autonomous agents integrated with VR software in order to make the ancient landscape & environment more lively: artificial life in the historical era one wishes, based on – and constrained by – scientific facts so as to be both useful for science and educational & entertaining for interested laymen.

A different strand of research is that of querying & reasoning, ontologies, planning and constraints.
Arbitrarily, I’ll start with the SIRENA project in Naples (the Spanish Quarter) [5], which aims to provide automatic generation of maintenance plans for historical residential buildings in order to make the current manual plans more efficient, cost effective, and maintain them just before a collapse. Given the UNI 8290 norms for technical descriptions of parts of buildings, they made an ontology, and used FLORA-2, Prolog, and PostgreSQL to compute the plans. Each element has its own interval for maintenance, but I didn’t see much of the partonomy, and don’t know how they deal with the temporal aspects. Another project [6] also has an ontology, in OWL-DL, but is not used for DL-reasoning reasoning yet. The overall system design, including use of Sesame, Jena, SPARQL can be read here and after server migration, their portal for the archeological e-Library will be back online. Another component is the webGIS for pre- and proto-historical sites in Italy, i.e., spatio-temporal stuff, and the hope is to get interesting inferences – novel information – from that (e.g., discover new connections between epochs). A basic online accessible version of webGIS is already running for the Silk Road.
A third different approach and usage of ontologies was presented in [7]. With the aim of digital archive interoperability in mind, D’Andrea et al. took the CIDOC-CRM common reference model for cultural heritage and enriched it with DOLCE D&S foundational ontology to better describe and subsequently analyse iconographic representations, from, in this particular work, scenes and reliefs from the meroitic time in Egypt.
With In.Tou.Sys for intelligent tourist systems [8] we move to almost-industry-grade tools to enhance visitor experience. They developed software for PDAs one takes around in a city, which then through GPS can provide contextualized information to the tourist, such as the building you’re walking by, or give suggestions for the best places to visit based on your preferences (e.g., only baroque era, or churches, or etc). The latter uses a genetic algorithm to compute the preference list, the former a mix of RDBMS on the server-side, OODBMS on the client (PDA) side, and F-Logic for the knowledge representation. They’re now working on the “admire” system, which has a time component built in to keep track of what the tourist has visited before so that the PDA-guide can provide comparative information. Also for city-wide scale and guiding visitors is the STAR project [9], bit different from the previous, it combines the usual tourist information and services – represented in a taxonomy, partonomy, and a set of constraints – with problem solving and a recommender system to make an individualized agenda for each tourist; so you won’t stand in front of a closed museum, be alerted of a festival etc. A different PDA-guide system was developed in the PEACH project for group visits in a museum. It provides limited personalized information, canned Q & A, and visitors can send messages to their friend and tag points of interest that are of particular interest.

Utterly different from the previous, but probably of interest to the linguistically-oriented reader is philology & digital documents. Or: how to deal with representing multiple versions of a document. Poets and authors write and rewrite, brush up, strike through etc. and it is the philologist’s task to figure out what constitutes a draft version. Representing the temporality and change of documents (words, order of words, notes about a sentence) is another problem, which [10] attempts to solve by representing it as a PERT/CPM graph structure augmented with labeling of edges, the precise definition of a ‘variant graph’, and a method of compactly storing it (ultimately stored in XML). The test case as with a poem from Valerio Magrelli.

The proceedings will be put online soon (I presume), is also available on CD (contact the WS organizer Luciana Bordoni), and probably several of the articles are online on the author’s homepages.

[1] A. Chella, I. Macaluso, D. Peri, L. Riano. RoBotanic: a Robot Guide for Botanical Gardens. Early Steps.
[2] G. Adorni. 3D Virtual Reality and the Cultural Heritage.
[3] M.C.Baracca, E.Loreti, S. Migliori, S. Pierattini. Customizing Tools for Virtual Reality Applications in the Cultural Heritage Field.
[4] E. Bonini, P. Pierucci, E. Pietroni. Towards Digital Ecosystems for the Transmission and Communication of Cultural Heritage: an Epistemological Approach to Artificial Life.
[5] A. Calabrese, B. Como, B. Discepolo, L. Ganguzza , L. Licenziato, F. Mele, M. Nicolella, B. Stangherling, A. Sorgente, R Spizzuoco. Automatic Generation of Maintenance Plans for Historical Residential Buildings.
[6] A.Bonomi, G. Mantegari, G.Vizzari. Semantic Querying for an Archaeological E-library.
[7] A. D’Andrea, G. Ferrandino, A. Gangemi. Shared Iconographical Representations with Ontological Models.
[8] L. Bordoni, A. Gisolfi, A. Trezza. INTOUSYS: a Prototype Personalized Tourism System.
[9] D. Magro. Integrated Promotion of Cultural Heritage Resources.
[10] D. Schmidt, D. Fiormonte. Multi-Version Documents: a Digitisation Solution for Textual Cultural Heritage Artefacts