La formation initiale et continue au Bénin : entre professionnalisation et déprofessionnalisation
Bibliographic record
Abstract
La fonction enseignante suscite au Bnin des dbats entre professionnalisation et dprofessionnalisation du fait des mesures de gratuit scolaire prises en 2006 en marge des prvisions du Plan dcennal de dveloppement du secteur de l' ducation, et qui ont conduit l'utilisation d' enseignants communautaires non qualifis pour faire face aux effectifs croissants. Le prsent article interroge doublement le rle de la formation initiale et continue d'une part et l'utilisation des enseignants communautaires d'autre part, sur la professionnalisation des enseignants et la qualit de l' enseignement. L' enqute qualitative ralise auprs de 53 lves matres en formation dans les coles normales d'instituteurs montre qu'au-del de l'utilisation des enseignants non qualifis se pose plus globalement le problme de la qualit de la formation des enseignants avec une tension entre des mesures favorables la professionnalisation et des effets relevant plutt de la dprofessionnalisation. Ainsi, la formation initiale ne semble apporter quasiment aucune plus-value et les donnes disponibles sur les rsultats des tablissements scolaires montrent que la prsence des enseignants communautaires n' est pas corrle la baisse de la qualit de l' enseignement. L'article fournit ainsi un ensemble d'informations susceptibles d'aider la prise en compte de la question enseignante dans la rforme de l' ducation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".