Book Review: Sabina Leonelli. Philosophy of Open Science. Cambridge: Cambridge University Press, 2023
Bibliographic record
Abstract
In Philosophy of Open Science, Sabina Leonelli engages with how the ideology behind "Open Science" (OS) is understood and undertaken within the contemporary academic community.This book draws primarily from theoretical research, but also from Leonelli's own experiences and perspective as an academic.It is a short read at around 60 pages, and the writing efficiently conveys relevant theories and its argument.I would recommend this book for anyone looking to successfully implement Open Science strategies and ideas.Because the book is precise in its argumentation, it would be most useful for those already a bit familiar with how Open Science, functions within the current research landscape, and who have some foundational knowledge on contemporary issues in philosophy of science.The book is still generally accessible for an academic audience and does provide brief descriptive accounts for ideas it engages with, although readers should be aware that brief explanations for complex ideas such as Knowledge Capitalism and Big Data can be insufficient to convey for the reader the true depth of those theories.The book begins with an exploration of notable historical developments in the philosophy of science which underpin the Open Science movement, including discussion of Robert K. Merton's principles of "Universalism" and "Disinterestedness", and Karl Popper's "Open Society".There is also an overview of the material history of knowledge sharing, knowledge resources, and journal publication.This historical lens helps situate Open Science and is illuminating in its own right.Building on these historical principles, the contemporary way that 'openness' in science is commonly understood is as a radical level of transparency where the final results and different aspects of the research process, such as datasets and methodology, are all free and available to anyone.This is indeed how 'open science' is being implemented in practice.However, Leonelli critiques this understanding of Open Science as being a very particular interpretation of "sharing science" within an object-oriented view of science.Furthermore, it is contended that this approach to sharing within
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".