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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".