Concevoir un programme d’études et ancrer ce travail de conception sur des propositions théoriques et méthodologiques, une tâche difficile ?
Bibliographic record
Abstract
En Belgique francophone, les programmes d’études ont généralement été construits par des praticiens (enseignants) et supervisés par un inspecteur, du moins dans l’enseignement officiel. Dans le cadre d’une recherche commanditée par la Fédération Wallonie-Bruxelles, les auteurs ont analysé les programmes d’études en sciences. Sur cette base, ils mettent en évidence quelques-unes des difficultés liées à la conception des programmes d’études. Les auteurs mettent notamment ce constat en parallèle avec la complexité d’identifier un modèle cohérent capable de soutenir la conception des programmes. Ils plaident, par ailleurs, en faveur d’un pilotage du processus de rédaction et proposent quelques pistes pour opérationnaliser les apports théoriques et méthodologiques dans le processus de conception.
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 | 0.112 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".