Mass Killings in Ontario: A Comparison of Domestic and Non-Domestic Killings
Bibliographic record
Abstract
Mass killings, defined as the killing of three or more victims in a short period of time, have received minimal attention in Canadian literature. Most mass killings involve male perpetrators who largely target females. Despite research showing that mass killings are a predominantly male-perpetrated crime, they are rarely recognized as a gendered phenomenon. The purpose of my study is to gain an understanding of what mass killings look like in Canada and explore domestic and non-domestic mass killings through a gendered theoretical perspective. Using a mixed-methods approach, I analyze 42 mass killings that occurred in Ontario between 1985 and 2012. My findings show that domestic and non-domestic mass killings share similarities (e.g., motivations) and differences (e.g., histories of domestic violence) and draw attention to the controlling nature of mass killers. Consequently, my research highlights mass killings as one extreme type of gender-based violence and emphasizes the need for further research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".