L’utilisation de mesures indirectes et directes du comportement dans l’évaluation des interventions ciblant les enfants agressifs
Bibliographic record
Abstract
L’efficacité des interventions auxquelles sont exposés les enfants agressifs doit être évaluée avec rigueur. Pour ce faire, les chercheurs et praticiens doivent disposer de mesures fiables, valides, exemptes de biais et qui soient suffisamment sensibles pour permettre de détecter tout changement dans le comportement des enfants. Le premier objectif de cet article consiste à mettre en évidence les limites associées à l’utilisation de mesures indirectes pour évaluer le comportement des enfants exposés à une intervention. Le second objectif vise à illustrer les enjeux associés à l’utilisation de mesures directes basées sur l’observation du comportement. Une analyse des différentes possibilités qui s’offrent aux chercheurs et aux praticiens, notamment en ce qui a trait au choix des comportements ciblés par l’observation et au contexte où se déroule l’observation, est proposée.
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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 | 0.129 | 0.218 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".