Bibliographic record
Abstract
Avec la guerre qui fait rage, tout autre sujet semble bien futile. Neanmoins, la SCPH et son Journal doivent prendre plusieurs decisions importantes de nature pecuniaire au cours des prochaines annees et j’aimerais partager avec vous quelques-uns des elements qui seront consideres dans la prise de decision. Il y a environ quatre ans, Harold Varmus, alors directeur du US National Institutes of Health, proposait que l’on concoive un site Web universel, « E-biomed », qui accepterait les articles provenant de tous les horizons de la biomedecine et donnerait a tous les lecteurs l’acces gratuit aux articles integraux 1 . Cette proposition sonnait le glas des imprimes, une vision apocalyptique qui a effraye certains editeurs de revues et nourri bien des commentaires negatifs 2 . En revanche, pour le nombre toujours croissant d’internautes, la nouvelle a ete recue positivement 3,4
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.021 |
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".