Consommation de psychotropes et délinquance : de bons prédicteurs de l’abandon scolaire ?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although empirical links between deviant behavior and school dropout have been extensively demonstrated, the specific influence of drug use and delinquency on school dropout is still not clear and varies across studies. One reason for this lack of consistency may rests upon the way samples of dropouts have been analysed. Recently, Janosz, Le Blanc, Boulerice and Tremblay (1996) constructed and validated a typology of school dropout highlithing the social and psychological diversity of this population. Using a longitudinal sample of adolescents (N=791), we analyzed the predictive relationships of family rebelliousness, drug use and delinquency on school dropout. The results showed an important variability in the predictive relationships according to the type of dropouts. The necessity of considering the psychosocial heterogeneity of dropouts when conducting such studies is discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it