Bibliographic record
Abstract
Despite the amount of research on police culture, little is known about how police culture reinforces systemic racism in Canadian policing. In race and policing scholarship, less is known about how women police officers perpetuate systemic racism in policing. Based on 16 interviews with women police officers from a police organization in Alberta, this study examines how police culture reinforces systemic racism in Canadian policing. Using colorblind racism and intersectionality, the findings demonstrate that officers regularly say racist jokes to normalize racialized police violence. Officers emphasize warrior police culture and suspiciousness to physically assault and racially profile Indigenous people, including those living in encampments. Officers associate Blackness with criminality by reinforcing culturally racist stereotypes about Black Canadians, such as having criminal lifestyles. Furthermore, officers hold racist and xenophobic perceptions about refugees and when refugees call the police for help, officers culturally frame them as criminals and blame all refugees for an individual's criminal offense. White women officers were more likely than Black women and Biracial (Indigenous/White) women officers to reinforce the colorblind racist myth that racialized police violence is only an American problem, and that policing is a race-neutral practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".