OVERVIEW OF PRIMARY SCHOOL PRINCIPALS' EDUCATIONAL LEVEL AND TRAINING IN BENIN: THE CHALLENGES RELATED TO THE EXPECTED COMPETENCIES AND SKILLS
Bibliographic record
Abstract
In Benin, very little research has been done on the profile of head teachers. It is therefore impossible to know, for example, to what extent the legal requirements regarding entry profiles are respected or whether headteachers are likely to possess the generic skills sought. This analysis could shed light on the recruitment and training policies for school principals in Benin in relation to the generic skills sought. This article contributes to filling this gap by analyzing the socio-demographic and professional characteristics of primary school principals in Benin. This exploratory study using descriptive analyses is carried out using data from the CONFEMEN Educational Systems Analysis Program (PASEC) collected during the 2013–2014 school year. Our results show a low feminization of school management positions in Benin, regardless of the status of the school (public or private). The professional certificate (CAP), the minimum diploma required to head a primary school in the Beninese education system, is held by all public-school principals. However, the private sector has more than 43 percent of principals who do not have the CAP. The study allows us to start thinking about whether or not it is necessary to impose a minimum level of schooling other than the vocational diploma (CAP) to be eligible for the position of head of a primary school. A second perspective concerns the importance of stricter supervision of private schools with regard to compliance with the regulations on appointment to the post of primary school principal in Benin.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".