<i>La mobilisation des diagnostics médico-psychologiques en sociologie : un passage obligé ?</i>
Bibliographic record
Abstract
Cet article présente une réflexion sur la mobilisation de catégories diagnostiques médico-psychologiques dans le cadre d’études qualitatives. Nous proposons une analyse réflexive des approches qui tendent à mettre au centre de leurs recherches les grilles diagnostiques comme manière de saisir les individus et leurs défis. À partir de lectures et travaux autour de ces objets, ce texte offre : 1) un bref rappel de la constitution de la sociologie de la santé mentale et de la place des catégories diagnostiques dans ce sous-champ sociologique; 2) une série de réflexions critiques sur la mobilisation de ces catégories dans la production scientifique contemporaine en sciences sociales; 3) des propositions pour prendre en compte de façon pertinente les effets de la mobilisation de catégories médico-psychologiques dans les différentes étapes du processus de recherche.
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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 | 0.105 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".