Bibliographic record
Abstract
Cet éditorial consiste en une recension des écrits décrivant le développement du mouvement des « meilleures pratiques ». Encore en partie méconnu dans le milieu de la pratique, ce mouvement est suffisamment important pour qu’une recension des écrits lui soit consacrée, d’autant plus que la littérature de langue française à ce sujet est pratiquement inexistante. Après avoir formulé une hypothèse économico-politique explicative à propos de leur émergence, l’article décrit les principaux modèles sous-jacents à leur développement : evidence-based model et expert-consensus model. Il suggère ensuite deux manières de présenter les meilleures pratiques pour faciliter leur utilisation : les lignes directrices et les algorithmes, de même que les interventions propres à favoriser leur dissémination auprès des praticiens et des administrateurs. L’article conclut par une analyse critique de cette approche.
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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".