Bibliographic record
Abstract
Déstigmatiser la santé mentale? J’ai beaucoup réfléchi à cette question en tentant de comprendre l’entêtement de notre héritage collectif stigmatisant à se transmettre dans le langage et les représentations liées à la santé mentale, puis en remettant en question ma posture professionnelle au contact d’adolescents et de jeunes adultes en situation de grande précarité pour lesquels a été conçu le centre de jour où j’ai oeuvré pendant plus de 20 ans. Ma pratique de proximité a représenté une occasion extraordinaire de participer à l’élaboration d’une éthique de solidarité et de coopération qui a permis de modifier le visage de la santé mentale. Déconstruire les préjugés, c’est prendre conscience de nos propres biais, revisiter les valeurs et principes qui guident notre culture de soins, accepter le déséquilibre nécessaire à toute transformation.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.075 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 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".