Bibliographic record
Abstract
Contexte : Le partage du savoir est impérieux. Ainsi vivent les revues. Pourtant, la production d’articles en pédagogie médicale demeure problématique dans le monde de la francophonie. L’auteur s’attarde à cette question, surtout à l’intention de la population de jeunes collègues qui ont opté pour ce domaine professionnel. Matériel : L’auteur utilise un article de la prestigieuse revue américaine Academic Medicine. Ces penseurs identifient neuf stratégies qui favorisent l’écriture d’articles en éducation médicale. L’auteur y ajoute ses réflexions et opinions grâce à l’expérience et au partage de confidences de collègues. Ceci conduit à identifier les activités les plus fructueuses. Résultats : Au-delà de ces processus stratégiques, l’auteur se risque à partager quatre observations personnelles qui toutes visent l’acquisition d’attitudes et suscitent la motivation interne, outils essentiels pour développer la « perspective universitaire » du goût de produire des articles. Conclusion : L’auteur aborde enfin la problématique de la reconnaissance universitaire de telles productions, l’enjeu de ce sujet.
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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".