Pour une société « suffisamment bonne » : reconnaître une pluralité de contributions et de parcours
Bibliographic record
Abstract
À partir des résultats d’une recherche qualitative et participative ayant impliqué des personnes qui vivent avec un problème de santé mentale, des intervenants et des gestionnaires de milieux de pratique en santé mentale et visant à explorer les représentations d’une intégration sociale jugée réussie, cet article propose une analyse et une réflexion sur les conditions de possibilité d’une société suffisamment bonne et inclusive pour les personnes vivant avec un problème de santé mentale. L’élargissement du prisme de la reconnaissance sociale s’est dégagé comme une condition essentielle à cet égard qui, elle-même, implique certaines conditions, avec en particulier un travail de reconnaissance de la différence en tant que telle et la mise à distance d’une logique d’utilité.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".