L’utilisation de l’évaluation dans le développement des interventions sociales
Bibliographic record
Abstract
L'évaluation peut apporter une contribution importante au développement des interventions auprès des personnes, des familles, des groupes et des collectivités en vue de réaliser des objectifs d'amélioration de leur situation sur le plan du bien-être. L'article explore la nature de l'évaluation des interventions sociales, ses caractéristiques, les différentes formes d'évaluation sur cas unique (études de cas, échelle d'appréciation de problème cible, échelle d'atteinte d'objectif, monitorage de cas et protocole avant-après avec ligne de départ) et la contribution de ces dernières à l'amélioration de la qualité et de l'efficacité des interventions sociales au bénéfice des populations. Sans nier la légitimité et l'importance des autres types d'évaluation, l'évaluation sur cas unique permet l'amélioration de l'imputabilité et le développement des connaissances en travail social et dans les autres professions d'intervention en sciences humaines.
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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 | 0.165 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".