Sexual Violence against Girls in Schools: Addressing the Gaps between Policy and Practice in Awaso, Ghana
Bibliographic record
Abstract
Despite the established relationship between girls' education and several social development outcomes, gender disparities in education remain particularly concerning. Among the many obstacles that still hinder girls’ access to quality education, sexual violence against girls in schools (SVAGS) is one of the most worrying but also one that has received the least attention in light of recent efforts to increase girls’ attendance in school. This article explores the interface between the seemingly solid Ghanaian legal and policy framework to protect children in educational institutions and the high incidence of SVAGS in such institutions. Its purpose is twofold: to identify the major barriers to fighting SVAGS in Awaso, a rural Ghanaian town, and to highlight strategies for lifting those barriers. Using classroom observation, focus group discussions and interviews with students, teachers, parents, NGO staff and government representatives, it explains how lack of knowledge, lack of financial resources, deep-set values and popular perceptions of masculinity, femininity and violence against women and girls contribute to SVAGS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".