The Social Environment of Daily Life and Perceptions of Police and/or Court Discrimination among African, Caribbean, and Black Youth
Bibliographic record
Abstract
Recent deaths of African-Americans at the hands of police and the creation of the protest movement Black Lives Matter has brought public attention to claims of racial profiling and police discrimination in the United States. In Canada, concerns about racial profiling centre on the disproportionate number of visible minority, particularly African-diasporic, individuals targeted in “street checks” or “carding.” This study used multivariate block logistic regression to compare the explanatory power of variables measuring different aspects of the social environment of daily life drawn from three theoretical frameworks (broken windows, routine activities, and critical race theory) when considering contact with police and perceptions of police and court discrimination in a sample of African-diasporic youth (N=529) who self-identify as African, Caribbean, or Black in a mid-sized city in Ontario, Canada. All measures of the social environment contributed significantly to explaining contact and perceived discrimination. In addition, the significant increase in the variance explained for both contact and perceived discrimination with each successive block supports the conclusion that consideration of the social environment of daily life contributes to an understanding of youth–police contact and youth perceptions of police and courts. The study's findings add to the discussion of inequality and youth–police contacts and experiences, as well as to the Canadian criminological literature exploring relationships between police and racialized youth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".