Engaging Boys in a Comprehensive Model to Address Sexual and Gender-Based Violence in Schools
Bibliographic record
Abstract
The prevention of sexual and gender-based violence (SGBV) requires attention to changing social norms, particularly gender norms, to promote gender-equitable attitudes and to reduce acceptance of violence. Among the many strategies that are being used to fight SGBV, an emerging theme has been the need to engage young boys and men. Over recent decades, frameworks have evolved to recognize not just women’s vulnerabilities to SGBV, but also the vulnerabilities of men to SGBV. There is also greater recognition that violence is used as an instrument to oppress both women and men, as well as young boys and girls, if they are perceived to act against established social norms. However, given that SGBV is predominantly directed against girls and women, it is critical to influence boys during formative periods of their lives to change their attitudes about gender roles and masculinity norms. We undertook a formative, exploratory qualitative research study to understand forms of SBGV experienced by adolescents in schools and to identify entry points to address it, primarily within school-spaces, in Bihar and Tamil Nadu (India). Findings strongly suggest that gender norms not only operate in the private lives of young girls and boys, but are heavily reinforced in schools through various practices and behaviors of teachers and management, as well as policies that differentiate girls and boys, leading to multiple experiences of physical, mental, and sexual violence for children over a prolonged period in their schooling years. Findings also point to weak response mechanisms to violence in schools and strong ‘normalization’ of violence by girls and boys as victims and perpetrators, respectively, but also as passive recipients of structural inequalities, pushing both genders into accepting a cycle of violence for the rest of their lives. Ultimately, this research offers a theory of change for intervention programs to address SGBV in school spaces.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".