Contribution à l’évaluation de l’efficacité des dispositifs de remédiation
Bibliographic record
Abstract
Cet article est tiré de mon travail de thèse préparée et soutenue à l’IREDU 1 – Université de Bourgogne. Ce travail est une réflexion sur un problème crucial d’échec universitaire en premier cycle, notamment en première année. Cette recherche examine l’efficacité de la filière « SPESS » (Semestre de préparation aux études scientifiques supérieures) mise en place pour tenter de parfaire l’insertion universitaire des étudiants faibles. Prenant appui sur les développements théoriques de l’estimation de la fonction de production des acquisitions et du modèle coût-efficacité, la partie empirique se focalise sur les modélisations linéaire et logistique associées respectivement à l’explication des différences de notes (sur 20) et à celle des chances de réussite finale (passage à l’ordre d’enseignement supérieur). Les résultats indiquent un effet négatif impressionnant du « SPESS ».
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".