Bibliographic record
Abstract
Marie Jauffret-Roustide et ses co-auteurs (Grégoire Cleirec, Jean-Maxence Granier, Benjamin Rolland) proposent un travail de réflexion sur la réduction des risques qui paraîtra dans sa globalité fin 2025.Le résumé publié aujourd'hui, « La réduction des risques, entre controverses, tensions et avancées autour des questions sanitaires, humanitaires et de tranquillité publique », nous en rappelle les fondamentaux.Déjà en 1983, Madame Rowan pointait la question de l'alcool chez les personnes âgées, un axe très souvent encore négligé.En 2024, Pascal Menecier s'est penché sur ce texte et nous montre sa pertinence.Dans les numéros de Psychotropes, régulièrement, un auteur alertait sur un nouveau produit, un nouveau risque, une nouvelle mode.Nous avions passé en 1986 un article passionnant de Peter Kalix sur le Khat.En 2024, Anne Batisse et les CEIP-A nous parlent du chemsex et des nouveaux produits de synthèse, dont… les cathinones.À suivre.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.163 | 0.014 |
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".