Increasing the evidence base in journalology: creating an international best practice journal research network
Bibliographic record
Abstract
Biomedical journals continue to be the single most important conduit for disseminating biomedical knowledge. Unlike clinical medicine, where evidence is considered fundamental to practice, journals still operate largely in a 'black box' mode without sufficient evidence to drive their practice. We believe there is an immediate need to substantially increase the amount and quality of research by journals to ensure their practice is as evidence based as possible. To achieve this goal, we are proposing the development of an international 'best practice journal research network'. We invite journals and others to join the network. Such a network is likely to improve the quality of journals. It is also likely to address many unanswered questions in publication science, including peer review, which can provide robust and generalizable answers.
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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 | 0.133 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".