The Effect of the Analyst-Officer Relationship on Crime Analysis: Experiential Knowledge vs. Data-Driven Decisions
Bibliographic record
Abstract
This article examines the importance of the relationship between police officers and crime analysts in the production and application of analyst outputs. Using qualitative interview data on ten analysts and two officers from one province in Canada, we illustrate the role and responsibilities of analysts, the effects of their relations with officers on their work, as well as the intended objectivity of crime analysis within intelligence-led policing (ILP). Specifically, we analyze the use of experiential knowledge by police officers in their patrols resulting in the underutilization of analyst products. The rampant miscommunication between officers and analysts leads to a cycle of misinformation, furthering the civilian-sworn divide present in police culture. As a result, it is revealed that analysts also exert experiential knowledge and discretion within their duties. We argue analysts and officers do not differ substantially in their knowledge production, as is previously believed in existing literature. The research is important to evaluate and understand how data driven policing is occurring and the ways it can be improved in the future.
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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 | 0.022 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".