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when open publishing is not fair [PCI webinar, 20 March, 4pm CET]

Posted in Books, University life with tags , , , , , , , , , , , on March 15, 2025 by xi'an

9th seminar of the PCI webinar series

Mandatory registration using this link: https://univ-cotedazur.zoom.us/meeting/register/rz8ZKReZQeqs-3yR3S6q4g

When Open Publishing Is Not Fair

Sabina Leonelli (Technical University of Munich, TUM)

Summary: There are obvious ways in which Open Access has augmented inequity rather than mitigating it, for instance in relation to Author Publishing Costs and the differential access that researchers based in academic institutions around the world may have to publishing deals and packages. Less obvious but equally fundamental are inequities in the access to infrastructures, skills and information fostering an effective use of online resources ranging from Open Access journals to Open Data infrastructures. Most importantly, openness as a paradigm of “sharing” is predicated on a model of research practice that does not fit well with most domains and methods of research, and particularly with science done in low-resourced environments. I reflect on these issues and draw on examples and cases emerging from the PHIL_OS project (“A Philosophy of Open Science for Diverse Research Environments”; www.opensciencestudies.eu ), as well as my experiences as Open Science advocate and participant in Open Access debates over the last ten years.

Speaker’s bio: After high school in Italy, Sabina Leonelli studied History and Philosophy of Science at University College London (BSc Hons, 2000) and London School of Economics (MSc, 2001). She earned a PhD from Vrije Universiteit Amsterdam (2007), while research assistant for Hasok Chang and attending the Dutch graduate schools for STS and philosophy. After coming back to LSE to work with Mary Morgan (2006-2008), she moved to the Centre for the Study of the Life Sciences at the University of Exeter, which she directed from 2013 to 2024. She was appointed TUM Professor on 09/24. Building on philosophical, historical and social science methods and collaborations with scientists and policy-makers, Sabina Leonelli studies: (1) the role of technology, data and organisms in knowledge production, and especially how computing and digitalisation efforts are transforming research and its social dynamics and roles; and (2) the institutionalisation of Open Science as a window on the methods, epistemology and political economy of contemporary scientific inquiry, particularly in the life, biomedical and environmental sciences.

What is the PCI webinar series?
-What is it? Seminars on research practices, publication practices, evaluation, scientific integrity, meta-research.
-How does it work? Remote conferences using zoom with registration.
-For whom is it? For anyone interested in scholarly publication, all PCI users, all PCI recommenders who do preprint evaluations for PCI, authors of articles, etc.
-When is it? Once a quarter
-Why is it for? To learn about scholarly publishing, to improve our knowledge about scholarly review, to become better reviewers, to create a sense of community among PCI users.

Find details about the PCI webinar series and past seminars at https://peercommunityin.org/pci-webinar-series/

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Le bayésianisme aujourd’hui

Posted in Books, Statistics with tags , , , , , , , on September 19, 2016 by xi'an

A few years ago, I was asked by Isabelle Drouet to contribute a chapter to a multi-disciplinary book on the Bayesian paradigm, book that is now soon to appear. In French. It has this rather ugly title of Bayesianism today. Not that I had hear of Bayesianism or bayésianime previously. There are chapters on the Bayesian notion(s) of probability, game theory, statistics, on applications, and on the (potentially) Bayesian structure of human intelligence. Most of it is thus outside statistics, but I will certainly read through it when I receive my copy.

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can we trust computer simulations? [day #2]

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , on July 13, 2015 by xi'an

Herrenhausen“Sometimes the models are better than the data.” G. Krinner

Second day at the conference on building trust in computer simulations. Starting with a highly debated issue, climate change projections. Since so many criticisms are addressed to climate models as being not only wrong but also unverifiable. And uncheckable. As explained by Gerhart Krinner, the IPCC has developed methodologies to compare models and evaluate predictions. However, from what I understood, this validation does not say anything about the future, which is the part of the predictions that matters. And that is attacked by critics and feeds climatic-skeptics. Because it is so easy to argue against the homogeneity of the climate evolution and for “what you’ve seen is not what you’ll get“! (Even though climatic-skeptics are the least likely to use this time-heterogeneity argument, being convinced as they are of the lack of human impact over the climate.)  The second talk was by Viktoria Radchuk about validation in ecology. Defined here as a test of predictions against independent data (and designs). And mentioning Simon Wood’s synthetic likelihood as the Bayesian reference for conducting model choice (as a synthetic likelihoods ratio). I had never thought of this use (found in Wood’s original paper) for synthetic likelihood, I feel a bit queasy about using a synthetic likelihood ratio as a genuine likelihood ratio. Which led to a lively discussion at the end of her talk. The next talk was about validation in economics by Matteo Richiardi, who discussed state-space models where the hidden state is observed through a summary statistic, perfect playground for ABC! But Matteo opted instead for a non-parametric approach that seems to increase imprecision and that I have never seen used in state-space models. The last part of the talk was about non-ergodic models, for which checking for validity becomes much more problematic, in my opinion. Unless one manages multiple observations of the non-ergodic path. Nicole Saam concluded this “Validation in…” morning with Validation in Sociology. With a more pessimistic approach to the possibility of finding a falsifying strategy, because of the vague nature of sociology models. For which data can never be fully informative. She illustrated the issue with an EU negotiation analysis. Where most hypotheses could hardly be tested.

“Bayesians persist with poor examples of randomness.” L. Smith

“Bayesians can be extremely reasonable.” L. Smith

The afternoon session was dedicated to methodology, mostly statistics! Andrew Robinson started with a talk on (frequentist) model validation. Called splitters and lumpers. Illustrated by a forest growth model. He went through traditional hypothesis tests like Neyman-Pearson’s that try to split between samples. And (bio)equivalence tests that take difference as the null. Using his equivalence R package. Then Leonard Smith took over [in a literal way!] from a sort-of-Bayesian perspective, in a work joint with Jim Berger and Gary Rosner on pragmatic Bayes which was mostly negative about Bayesian modelling. Introducing (to me) the compelling notion of structural model error as a representation of the inadequacy of the model. With illustrations from weather and climate models. His criticism of the Bayesian approach is that it cannot be holistic while pretending to be [my wording]. And being inadequate to measure model inadequacy, to the point of making prior choice meaningless. Funny enough, he went back to the ball dropping experiment David Higdon discussed at one JSM I attended a while ago, with the unexpected outcome that one ball did not make it to the bottom of the shaft. A more positive side was that posteriors are useful models but should not be interpreted from a probabilistic perspective. Move beyond probability was his final message. (For most of the talk, I misunderstood P(BS), the probability of a big surprise, for something else…) This was certainly the most provocative talk of the conference  and the discussion could have gone on for the rest of day! Somewhat, Lenny was voluntarily provocative in piling the responsibility upon the Bayesian’s head for being overconfident and not accounting for the physicist’ limitations in modelling the phenomenon of interest. Next talk was by Edward Dougherty on methods used in biology. He separated within-model uncertainty from outside-model inadequacy. The within model part is mostly easy to agree upon. Even though difficulties in estimating parameters creates uncertainty classes of models. Especially because of being from a small data discipline. He analysed the impact of machine learning techniques like classification as being useless without prior knowledge. And argued in favour of the Bayesian minimum mean square error estimator. Which can also lead to a classifier. And experimental design. (Using MSE seems rather reductive when facing large dimensional parameters.) Last talk of the day was by Nicolas Becu, a geographer, with a surprising approach to validation via stakeholders. A priori not too enticing a name! The discussion was of a more philosophical nature, going back to (re)define validation against reality and imperfect models. And including social aspects of validation, e.g., reality being socially constructed. This led to the stakeholders, because a model is then a shared representation. Nicolas illustrated the construction by simulation “games” of a collective model in a community of Thai farmers and in a group of water users.

In a rather unique fashion, we also had an evening discussion on points we share and points we disagreed upon. After dinner (and wine), which did not help I fear! Bill Oberkampf mentioned the use of manufactured solutions to check code, which seemed very much related to physics. But then we got mired into the necessity of dividing between verification and validation. Which sounded very and too much engineering-like to me. Maybe because I do not usually integrate coding errors and algorithmic errors into my reasoning (verification)… Although sharing code and making it available makes a big difference. Or maybe because considering all models are wrong is neither part of my methodology (validation). This part ended up in a fairly pessimistic conclusion on the lack of trust in most published articles. At least in the biological sciences.

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can we trust computer simulations?

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , on July 10, 2015 by xi'an

lion

How can one validate the outcome of a validation model? Or can we even imagine validation of this outcome? This was the starting question for the conference I attended in Hannover. Which obviously engaged me to the utmost. Relating to some past experiences like advising a student working on accelerated tests for fighter electronics. And failing to agree with him on validating a model to turn those accelerated tests within a realistic setting. Or reviewing this book on climate simulation three years ago while visiting Monash University. Since I discuss in details below most talks of the day, here is an opportunity to opt away! Continue reading →

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snapshot from Hannover

Posted in pictures, Running, Travel, University life with tags , , , , , , on July 9, 2015 by xi'an

Rathaus

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