Why’s Everybody Always Pickin’ on me? A New Look at Police/Minority Contact
Bibliographic record
Abstract
Presently there are two explanations for disproportionate minority/police contact: racism and a belief that minorities commit most crimes and there is a need to focus on those communities. This article examines a third possibility that focuses on policing as a social service and minority use of social services in our society. The research examines policing as a social service and compares minority use of other social services with their use of police services. The research also looks at pulling of police into neighborhoods by measuring calls-for-service in various communities; it examines police resource allocation which, as the research indicates, is significantly based on these calls-for-service; and compares minority use of police services with minority utilization of other social services. The research supports the premise that disproportionate minority contact by police is a social phenomenon that is similar to minority over-utilization of other social services.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".