L’alcoolisation à risque chex les femmes au travail : l’expression d’un mal-être professionnel
Bibliographic record
Abstract
Les résultats présentés ici proviennent d'une recherche qualitative, réalisée auprès de vingt-cinq femmes au travail et catégorisées consommatrices d'alcool à risque, au moyen du SMAST (Short Michigan Alcoholism Screening Test). Le but de l'étude était de cerner les conditions sociales d'émergence de l'alcoolisation à risque chez ces femmes et les mécanismes de son développement. Cet article comprend trois parties. L'auteure discute d'abord comment s'opère le passage du boire social au boire à risque et pourquoi les femmes choisissent l'alcool; elle présente ensuite trois modèles génésiaques, c'est-à-dire les voies ou les situations professionnelles qui conduisent les femmes au boire à risque; enfin, elle décrit trois modes évolutifs : un mode régressif, une habitude et un mode progressif.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".