Police personnel allocation and homicide clearance
Bibliographic record
Abstract
Purpose This paper applies novel techniques from the field of operations management to examine the allocation of patrol and investigative personnel to identify which is most effective in improving police performance around homicide clearance. Design/methodology/approach A panel sample of homicide clearance rates from the 100 largest US cities between 2000 and 2013 were analyzed in two steps: first, a random-effects regression model was performed to locate influential factors; second, optimum analysis was applied to locate the optimal values that yield maximal homicide clearance. Findings Both patrol and investigative personnel levels have a significant impact on homicide clearance. Maximal clearance can be achieved by allocating departmental personnel to investigative roles. Research limitations/implications Given recent trends around “defunding” police and public sector austerity measures, future research should continue to explore the utility of optimum analysis for efficient allocation of policing personnel. Originality/value This study provides proof of concept for the use of optimum analysis in policing research.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".