Racially biased policing and neighborhood characteristics: A Case Study in Toronto, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated race-and-place profiling in Toronto within a neighborhood context. It explored the spatial association between race-specific drug-related stops and neighborhood racial and socio-economic characteristics. The findings of this study suggest that Blacks are subject to disproportionately more stops for drug-related reasons in neighborhoods where more Whites reside and are less socio-economically disadvantaged, therefore confirming race-and-place profiling of Blacks in Toronto. However, race concentration and socio-economic disadvantage arguments fail to explain the spatial variations in drug-related stops of Whites. This result could be caused by the diverse ethnic origins and socio-economic backgrounds of White Torontonians. This article argues for the importance of a contextualized examination of racial profiling within the spatial context of neighborhoods and calls for democratic policing in Toronto. It also discusses the negative impacts of race-and-place profiling on Blacks in Toronto.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it