Bibliographic record
Abstract
Proponents of data-driven policing strategies claim that it makes policing organizations more effective, efficient, and accountable and has the potential to address some policing social criticisms (e.g. racial bias, lack of accountability and training). What remains less understood are the challenges when adopting data-driven policing as a response to these criticisms. We present results from a qualitative field study about the adoption of data-driven policing strategies in a Midwestern police department in the United States. We identify three key challenges police face with data-driven adoption efforts: data-driven frictions, precarious and inactionable insights, and police metis concerns. We demonstrate the issues that data-driven initiatives create for policing and the open questions police agents face. These findings contribute an empirical account of how policing agents attend to the strengths and limits of big data's knowledge claims. Lastly, we present data and design implications for policing.
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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 | 0.178 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.024 | 0.103 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".