Not So Random Acts of Violence: Shared Social-Ecological Features of Violence Against Women and School Shootings
Bibliographic record
Abstract
The current study examines an understudied potential warning sign of school shootings: violence against women (VAW). Utilizing the social-ecological model of violence prevention, we employed directed content analysis to determine the prevalence of acts and social-ecological features of VAW among profiles of 59 boys/men who perpetrated school shootings between 1966 and 2018. The majority of shootings profiled occurred in the United States (47, 79.7%), followed by Canada (5, 8.5%), Finland (2, 3.4%), Germany (2, 3.4%), Brazil (1, 1.7%), Scotland (1, 1.7%), and Ukraine (1, 1.7%). Results demonstrated a strong presence of VAW among profiled school shooters, with almost 70% perpetrating VAW and the identification of frequent features of VAW that cut across the social-ecological levels, most notably (the enactment of and failure to meet expectations of) hegemonic masculinity and normalization of violence. Implications for research and intervention are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".