Mesure et Analyse de l’Efficacité des Etablissements de la Formation Professionnelle en Tunisie
Bibliographic record
Abstract
Le présent travail a pour objectif de mesurer et d’analyser l’efficacité des établissements de la formation professionnelle en Tunisie. Pour cela, une méthodologie en deux étapes a été utilisée. La première étape concerne l’estimation de l’efficacité technique par le modèle BCC (1984). La deuxième étape estime l’efficacité allocative en utilisant les modèles des coûts d’ombre: Kumbhakar (1996) et Balk (1997). Les principaux résultats montrent, d’une part, que ces établissements peuvent être classés, par niveau d’efficacité technique, en trois groupes. Le groupe hautement efficace, dont les scores dépassent 0.8, renferme 20% des établissements. Le groupe moyennement efficace, dont les scores sont entre 0.5 et 0.79, renferme 46%. Enfin, le groupe faiblement efficace, avec des scores inferieurs à 0.5, contient le reste des centres. D’autre part, les résultats montrent que les centres sont allocativement efficaces (> 0.8). This paper aims to measure and analyze efficiency of public training establishments in Tunisia. For that, a two-stage methodology is used. The first stage estimates technical efficiency scores using the BCC model (1984). The second, based on results of the first stage, estimates allocative ones using shadow cost models: Kumbhakar (1996) and Balk (1997). The main results provide, on the one hand, that establishments could be classified, by technical efficiency level, into three groups. The most efficient group, with scores higher than 0.8, includes 20% of establishments. The middle efficient group, with scores between 0.5 and 0.79, represents 46%. Finally, the rest (34%) represent a lower efficient group with scores under 0.5. However, results show that centers are allocatively efficient (>0.8).
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".