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Record W2095967350 · doi:10.6000/1929-4409.2013.02.26

Why’s Everybody Always Pickin’ on me? A New Look at Police/Minority Contact

2013· article· en· W2095967350 on OpenAlexvenueno aff
Robert Werling, Patricia A. Cardner

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsCommitCriminologySocial workPremisePublic relationsService (business)RacismSociologyPolitical scienceLawBusinessGender studies

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.013
Scholarly communication0.0080.018
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.105
GPT teacher head0.399
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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