Examining Gender Safety in Schools: Teacher Agency and Resistance in Two Primary Schools in Kirinyaga, Kenya
Bibliographic record
Abstract
This article introduces Stein, Tolman, Porche, and Spencer’s concept of gender safety in schools (GSS) as a useful framework for providing a gendered analysis of safety and equality at the school level within the global context of the Sustainable Development Goal (SDG) 4 goal of equitable, inclusive and quality education for all. This article examines practices that support as well as undermine GSS in two primary schools in Kirinyaga County, Kenya. In these schools, individual teacher agency was the main factor enhancing GSS. Teachers’ efforts were, however, constrained by competing discourses emphasizing hierarchical administration and a narrow understanding of the school’s responsibilities. Teacher agency, therefore, was insufficient to systematically protect students and foster gender equity. The article suggests that teacher agency to enhance GSS in Kenya could be expanded through teachers’ collective empowerment using community-based networks alongside the integration of monitoring and evaluation processes in existing gender equality and child protection policies. It further recommends the GSS framework as a means for monitoring SDG 4’s commitments to gender equality and child protection in schools.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".