LES POLITIQUES DE LA BANQUE MONDIALE RELATIVES À LA PROMOTION DE L’ÉDUCATION DES FILLES DANS LES PAYS EN DÉVELOPPEMENT: PROMESSE D’ÉQUITÉ OU RENFORCEMENT DES INÉGALITÉS?
Bibliographic record
Abstract
Cet article porte sur l’analyse des politiques privilégiées par la Banque mondiale pour promouvoir la scolarisation des filles dans les pays en développement. Lʹexamen est fondé sur une approche sociopolitique des rapports sociaux de sexe, des représentations sociales de l’éducation et de la place et du rôle des filles et des femmes dans la société. L’analyse des rapports annuels et des plans stratégiques révèle que les politiques éducatives de l’institution sont conformes à l’idéologie néolibérale et à une vision réductrice et utilitariste du capital humain considérant les filles et les femmes comme une ressource productive essentielle à ne pas gaspiller. Mots clés : scolarisation, équité, filles, pays en développement, analyse des politiques This article reports on an analysis of World Bank policies encouraging school enrolment of girls in developing countries. The study is based on a sociopolitcal approach to gender relations, social representations of education, and the role of girls and women in society. The analysis of annual reports and strategic plans shows that the Bank’s educational policies are in line with a neoliberal ideology and a reductionist and utilitarian view of human capital that sees girls and women as an essential productive resource that should not be wasted. Keywords: school enrolment, equity, girls, developing countries, policy analysis
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".