Cooperative Non-APC Publishing Models: Canada, Europe and Latin America
Bibliographic record
Abstract
Presentations and recording from a joint AmeliCA/Canadian Research Knowledge Network/Coalition Publica/OpenAIRE webinar. Discussion about non-APC strategies, challenges and recommendations and a global collective action. Arianna Becerril-García (Executive Director, Redalyc, Professor, UAEM, Chair, AmeliCA) talks about AmeliCA - a multi-institutional community-driven initiative supported by UNESCO and led by Redalyc and CLACSO aimed to provide a cooperative, sustainable, protected and non-comercial infrastructure for Open Knowledge. Tanja Niemann (Executive Director, Érudit) and Jason Friedman (Manager, Member and Metadata Services, Canadian Research Knowledge Network) talk about Coalition Publica and the Partnership for Open Access: Canada's Cooperative Non APC Publishing Model. Jean-Claude Guédon discusses the current landscape, challenges, collaborations, "inside-out" libraries (Lorcan Dempsey's vocabulary), and provides recommendations about fostering a richer bibliodiversity and ensuring publication and access equality for all. And Iryna Kuchma presents the OpenAIRE report Towards Sustainable Cooperative and Non-APC Publishing Model: D6.2 – Best Practice Guide for Co-Operative Models of Publishing.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".