Towards a Macro-Level Theoretical Understanding of Police Services’ Acquisition of Risk Technologies
Bibliographic record
Abstract
Most North American police services have rapidly acquired and implemented a range of emerging and disruptive technologies in recent years. This rapid adoption of technologies has left a significant gap in our theoretical understanding of how police make decisions about which technologies to acquire. While existing research has focused on technology’s impact at the organizational level, the macro-level context that shapes technological acquisition by the police is undertheorized. To address this gap in the literature, this article combines theorizing by Ericson and Haggerty (1997) on policing the risk society (PRS) and Zuboff (2019) on surveillance capitalism (SC) to develop a macro-level theoretical framework. We consider technologies acquired by the police to be risk technologies and argue that combining key elements of PRS and SC theorizing offers a macro-level understanding of police decision-making about which technologies to adopt that can complement meso-level organizational theories. While calling for additional empirical research, this article concludes by discussing the potential impacts associated with private-sector involvement in public-sector initiatives and providing directions for future 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".