An International Perspective on Under-representation of Female Leaders in Kenya’s Primary Schools
Bibliographic record
Abstract
Studies of school administrators in North America, Europe, and Australia have shown consistently that women, although a majority in the teaching force, are under-represented in leadership positions. This study examine whether the factors associated with under-representation of women in school administration in Kenya are the same as they are in other countries. The female participants identified six barriers. These are, family obligations, cultural beliefs, unethical recruitment practices, lack of networking, low expectation of success, lack of role models and mentors. The participants further identified four areas for improvements. These are, educating the public, training and exemplars, and recruitment. Les recherches sur les administrateurs des écoles en Amérique du Nord, en Europe, et en Australie ont montré régulièrement que les femmes, qui pourtant font la majorité de l'effectif des enseignants, sont toujours sous-représentées dans les positions de dirigeants. Cette recherche a pour but d'examiner si les facteurs associés à cette sous représentation sont les mêmes que dans les autres pays. Les participantes à cette recherche ont identifié six obstacles. Ce sont: les obligations familiales, les croyances culturelles, les pratiques peu éthiques de recrutement, le manque de gestion de réseau, une espérance non élevée de succès, et le manque de modèles à émuler ou de mentors. Les participantes ont ensuite identifié quatre domaines où l'on pourrait effectuer les améliorations. Ce sont: l'éducation du public, la formation, des modèles, et le recrutement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".