Male Police involved Intimate Partner Violence: Is it more Dangerous than Abuse by Civilians? An Argumentative Analysis
Bibliographic record
Abstract
In the wake of the killing of George Floyd, Canadian and US policing has received mass scrutiny. These violent police behavior is currently prompting questions about the appropriate use of force by police officers. Police use of force usually focuses on police officer’s brutality in their official capacity, but there is a lack of empirical data on brutality and abuse perpetrated by police against their intimate partners in their private family lives. Although police officers are more likely than civilians to abuse their partners, the power and training provided to police officers by the state make them significantly more dangerous as domestic violence perpetrators. “Feminist theory” and “Hegemonic Masculinity theory” have been applied to understand the power and control that police officers have due to their training, which causes them to be considered more violent and dangerous for their families, especially for their intimate partners. To support the argument, three key themes have emerged: police abusers are skilled abusers; victims face a tremendous struggle to report the abuse; and available support systems for the victims of police abusers sometimes not adequate for them. Finally, some policy implications have been provided to address this problematic and potentially more dangerous issue.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".