Assessing what police officers do “on the job”: toward a “public values” approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the results of a test in a policing context of a performance assessment tool that is based on a “public values” approach. The Capability, Importance, and Value (CIV) Tool allows police organizations to examine the roles their members carry out to determine whether they are being capably done, are important, and deliver value to stakeholders. Design/methodology/approach Five focus groups were conducted with front line officers from a large Canadian police service. The focus group process incorporated elements of Appreciative Inquiry and Structured Brainstorming. Findings Valuable information can be collected from front line police officers with the CIV Tool. Police organizations could use this information to improve performance while ensuring that the roles undertaken by their members align with broader organizational goals and objectives including providing value to stakeholders. Research limitations/implications This study was designed as a limited test of the CIV Tool. More extensive testing is required with a larger sample that includes police investigators and members of other specialty units. Practical implications The CIV Tool can serve to augment existing police performance measurement strategies. It can help to identify which roles contribute to achieving organizational goals and which do not. Based on this information, ameliorative action can be taken. Social implications A “public values” approach places emphasis on stakeholder needs and expectations. Addressing these directly can result in enhanced performance as well as greater police transparency, responsiveness, and accountability. Originality/value Ongoing police performance assessment based on a “public values” approach is uncommon in policing. Its use has important implications for police organizations and their stakeholders.
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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 | 0.083 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".