Intersectional Inequality: An Analysis of Police Culture in a Western Canadian City
Bibliographic record
Abstract
Despite the plethora of research on police culture, few studies have examined police culture from an intersectional approach. To provide more intersectional research on police culture, I conducted 16 semi-structured interviews with women police officers from a police organization in Alberta to explore how they perceive and experience police culture. I find that women police officers witness and/or experience three types of workplace violence: physical violence; bullying, harassment, and intimidation; and lateral violence. Black women, Biracial (Indigenous/white) women, white women and LBGTQ2SIA+ white women report having to deescalate violent situations whenever police officers, predominantly men, commit acts of physical violence on members of the public. Black women, Biracial (Indigenous/white) women, white women and white LGBTQ2SIA+ women police officers reported experiencing various forms of bullying, harassment, and intimidation, including misogynoir, race, and gender-based harassment, sexual harassment, and homophobia. Women also report women partaking in lateral violence by competing and sabotaging other women to advance their career. I also found that anti-Indigenous racism, anti-Black racism, and xenophobia is major problem in police culture. Many examples of racism in police culture included police officers saying racist jokes on-duty and in the office; physically abusing, racially profiling, and harassing Indigenous peoples, including those experiencing homelessness; anti-Black racism in homicide investigations and officers shouting racist and xenophobic slurs at refugees. Although white women were more likely than women of colour to acknowledge systemic racism in policing, they often used colourblind interpretations to underestimate the existence of racism in police culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".