Regards croisés sur les phénomènes de violence en milieu scolaire : élèves et équipes éducatives
Bibliographic record
Abstract
Le présent article vise à répondre aux questions suivantes, tant du point de vue des élèves que de celui des enseignants : (1) Quelle est la fréquence de différents types de victimation à l’école et en dehors de l’école ? (2) Quels sont les facteurs qui permettent de prédire ces victimations ? (3) Quel est l’impact de ces victimations sur le sentiment d’insécurité, le bien-être subjectif et les conduites ? Une enquête de victimation a été menée auprès d’un échantillon représentatif de l’enseignement secondaire belge francophone. Les résultats suggèrent que les pratiques d’enseignement, la cohérence des équipes éducatives et le comportement de la direction ont une influence notable sur la fréquence des victimations et sur la qualité de vie à l’école.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".