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<strong> </strong>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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".