Fidélité, validité discriminante et prédictive de l’indice de prédiction du décrochage.
Bibliographic record
Abstract
Le decrochage scolaire touche une forte proportion de jeunes de tous les milieux. Neanmoins, il demeure plus frequent chez les garcons de milieux defavorises. A l'heure actuelle. les principaux facteurs (sociaux, familiaux, scolaires) qui predisent le decrochage sont largement documentes dans les ecrits scientifiques. Par contre, les outils de depistage permettant de cibler efficacement les jeunes a risque de decrocher demeurent peu nombreux. L'objectif de l'etude etait d'etablir la fidelite et la validite de l'Indice de prediction du decrochage (IPD), un indicateur permettant de depister les decrocheurs potentiels. Les qualites psychometriques de cet indicateur ont ete evaluees au moyen d'un echantillon longitudinal (3 ans) de 35 068 eleves (47,2 % de garcons) âges de 13 a 16 ans. Ils provenaient de 79 ecoles secondaires qui participaient a l'evaluation de la strategie d'intervention Agir autrement (SIAA). Les resultats de l'etude revelent que l'IPD presente une bonne stabilite ainsi qu'une bonne capacite discriminante et predictive. Il s'agit d'un indicateur pertinent pour depister les jeunes a risque de decrocher et ainsi favoriser l'implantation efficiente de programmes de prevention ciblee.
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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".