Faire parler la professionnalisation des pratiques bénévoles en santé
Bibliographic record
Abstract
Dans un contexte de professionnalisation des organisations de santé, et conséquemment des pratiques bénévoles, notre article vise à explorer cette tendance via un terrain ethnographique réalisé dans un établissement hospitalier universitaire du Québec. Plus spécifiquement, nous mettrons en lumière 1) en quoi ce type d’organisation en santé vouée à l’excellence oriente l’expérience bénévole 2) comment les bénévoles font parler cette professionnalisation au quotidien. Pour rendre compte de la complexité du caractère professionnalisant de la pratique bénévole, nous avons opté pour une perspective ethnographique. Par ailleurs, nous procéderons à l’analyse des données selon une approche constitutive de la communication, – aussi appelée CCO –, avec l’apport analytique de la ventriloquie.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".