Doing medical journals differently: Open Medicine, open access, and academic freedom. Canadian
Bibliographic record
Abstract
Abstract: With considerable attention now being paid within scholarly commu-nications to publication models that increase access to research, the launch of the open access journal Open Medicine demonstrates the contribution that open access, in all of its various economic models, can make to scholarly traditions of editorial independence, intellectual integrity, and academic freedom. This paper details the history of Open Medicine, which was born of an editorial-interfer-ence incident in the field of medical publishing, and offers a case study of the current political economy of academic publishing. This new journal demon-strates how open access, in combination with open source publishing and man-agement software, enables new journals to more readily protect the academic freedom of researchers and scholars. As we argue, this method of publishing provides a venue for the emergence of new approaches, ideas, and independence from sources of competing interests in scholarly 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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".