Bibliographic record
Abstract
All children, young people and adults can be the targets of violence, the most likely perpetrators being family members, intimate partners or acquaintances. No one deserves such abuse and we must continue to explore ways to prevent all violence. Nonetheless, it is critical to acknowledge that girls and women are the primary targets of many forms of abuse, including child sexual abuse, dating violence, intimate partner violence, sexual assault, and sexual harassment (Heise et al, 2002). Such forms of violence are defined as ‘gender-based’, occurring in private (as in abuse in families and intimate relationships) and public spheres (school, community). What causes gender-based violence? While several authors have adopted a multi-levelled, ecological view of the etiology of such violence (Heise et al, 2002), one significant cause within this is a power imbalance related to male authority and privilege (Morrison et al, 2007). Gender-based violence is particularly evident when societal attitudes, behaviours and institutions uphold traditional male power. The fear of violence experienced by many women and girls tends only to reinforce the gender inequality in society; reinforcing a sense of powerlessness and limiting the effective functioning of girls and young women in both private and public realms (Berman et al , 2002). School-based violence prevention programmes are one important way to inform, address and provide strategies to intervene so that violence either does not occur or its effects are minimised. Developed in the past 30 years, a proliferation of prevention programmes address school violence, bullying, sexual abuse, dating violence, discrimination, sexual harassment, sexual assault and the sexual exploitation of children and young people (Tutty et al, 2005). Drawing on the Canadian experience, this chapter explores the role and importance of a gendered framework in preventing violence against women and girls. In so doing it presents sections on school-based programmes addressing the prevention of child sexual abuse, bullying, sexual harassment, dating violence and, briefly, sexual assault, examining the gendered aspects of each. Although many programmes cite such gendered violence as a core principle, the extent to which this is explicit in the programme materials varies. The chapter discusses some of the challenges of adopting a gendered approach and strategies to address this in gaining entry to the school system. One approach to highlight the gendered nature of intimate violence is administering the programme or parts of the programme in separate gender groups.
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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 | 0.052 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".