Frais d’inscription dans l’enseignement supérieur et régimes d’État-providence : une analyse comparative
Bibliographic record
Abstract
Les expériences nationales de mise en place de frais d’inscription apparaissent très variées dans la littérature et par leurs effets sur les étudiants. Leurs résultats contrastés sont à interpréter et à mettre en perspective dans leurs environnements institutionnels respectifs. L’auteur définit la notion d’institution pour analyser le système d’enseignement supérieur et dresser une typologie des différents régimes institutionnels. Le concept de régimes d’État-providence d’Esping Andersen (1990, 1999) est utilisé pour comprendre les approches nationales de financement et les comparer. Leur analyse met en évidence une relation forte entre ces politiques et les régimes d’État-providence. Si celui de type conservateur ne semble pas viable à long terme, les régimes social-démocrate et libéral renvoient à deux logiques distinctes de l’éducation : investissement collectif contre investissement individuel.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".