Race, Gender, and Police Violence in the Shadow of Controlling Images
Bibliographic record
Abstract
Abstract Despite the emergence of the #SayHerName movement alongside #BlackLivesMatter, research on police encounters is rarely intersectional and has largely neglected the potentially violent consequences of gendered and racialized “controlling images.” Using New York City investigatory stop data (2007–2014), and drawing on controlling images theory, our analysis shows that Black men and women experience higher rates of police violence than White men and women. Within race, analyses indicate that Black men experience more police violence than Black women. The same gender gap exists for Whites, Asians, and Latinx persons, suggesting that broad cultural perceptions of femininity and masculinity shape police violence. However, these gendered frames mostly dissolve in instances of potentially fatal violence, as we find no gender differences within race or ethnicity in these extreme cases with one exception: police point their guns at Black men slightly more than at Black women. Further, the controlling image criminalizing Black men casts a long shadow—police are more likely to use violence on individuals stopped in the company of a Black man across gender, race, and ethnicity. This study provides a comprehensive, intersectional analysis of police encounters, both reaffirming and extending controlling images to understand why race, ethnicity, and gender disparities in state violence experiences persist.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".