L’évaluation de l’efficience des institutions d’enseignement supérieur en Tunisie : le cas des Instituts Supérieurs des Études Technologiques (ISET)
Bibliographic record
Abstract
Dans cet article, nous évaluons l’efficience des Instituts supérieurs des études technologiques tunisiens (ISET) avec la méthode non paramétrique du Data Envelopment Analysis (DEA) . Il ressort des résultats empiriques que le fonctionnement de ces établissements se caractérise par une inefficience technique de l’ordre de 20 %, pouvant s’expliquer à la fois par des problèmes de taille des établissements (inefficience d’échelle de 11 % environ) et par des problèmes de gestion (inefficience pure de l’ordre de 10 %). Les résultats montrent également qu’une majorité des ISET de notre échantillon fonctionnerait de façon optimale si leur échelle de production augmentait, ce qui peut se révéler des pistes de solutions pour les pouvoirs politiques voulant promouvoir le système éducatif supérieur en Tunisie.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".