The Self-Reported Use of Social Equity Indicators in Urban Police Departments in the United States and Canada
Bibliographic record
Abstract
a way to evaluate the delivery of services to the public (see, for example, Hatry 2006; Berman and Wang 2000; Rivenbark and Kelly 2004; Dougherty, Lindquist, and Bradbury 2006; Krane 2008). Moreover, a host of different measures have been devised to evaluate gov ernment performance and ultimately service quality (Hatry 1980). However, the literature reveals that measures or indicators of perfor mance often focus on efficiency and effective ness, key values that have dominated public administrative practices. Consequently, other important measures of performance that are valued by governments and the citizenry are ignored and go undetected. One such value is social equity. This exploratory study examines the ex tent to which police departments across the United States and Canada report that they have factored social equity into their perfor mance measurement programs. The results of a survey administered to 148 randomly selected police departments and a content analysis of their public documents provide a preliminary look at the degree to which so cial equity is reported to be an indicator of performance. Also examined are the potential factors that lead police departments to adopt social equity indicators. This article begins with a brief review of the reliance on performance measurement in the public sector generally and then looks specifically at performance measures in polic ing. The area of policing was selected given the importance of values such as social equity to this profession and the social repercussions if such values are ignored. The evaluation of the extent to which social equity indicators are present is followed by a systematic analy sis of the factors that help explain variations in the use of these indicators as performance measures.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".