Une estimation des frais de traitement des articles. Comment l'oligopole des éditeurs savants profite-t-il du libre accès | Estimating article processing charges. How the oligopoly of academic publishers profits from open access
Bibliographic record
Abstract
Invited presentation, Tri-Agency Open Science Executive Committee Bilingual (French-English), 28.10.2022, 13-14h Dans le cadre de la Semaine internationale du libre accès 2022, le Comité exécutif des trois organismes sur la science ouverte vous invite à nous joindre pour un exposé sur une étude récente de l'Université d'Ottawa sur les frais de traitement d'articles (APC) payés pour publier dans des revues en libre accès (ou hybrides) contrôlées par les grands éditeurs commerciaux Elsevier, Sage, Springer-Nature, Taylor & Francis et Wiley, le soi-disant oligopole de l'édition académique. La conférence permettra aux participants d'avoir une idée des tendances actuelles de l'édition or et hybride, des APC et leurs impacts sur le libre accès. La présentation sera suivie d'une période questions et réponses avec les auteurs. As part of International Open Access Week 2022, the Tri-Agency Open Science Executive Committee is inviting you to join us for a talk on a recent University of Ottawa study of article processing charges (APCs) paid to publish in open access journals (gold and hybrid) controlled by the large commercial publishers Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley, the so-called oligopoly of academic publishing. The talk will allow participants to gain an appreciation of current trends in hybrid and gold publishing and APCs and their impacts on open access. The talk will be followed by a Q and A with the authors.
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".