Why policing the risk society became a footnote in American police studies: A missed opportunity to move police theorizing forward
Bibliographic record
Abstract
Written in 1997, Ericson and Haggerty’s book Policing the Risk Society (PRS) should have had profound effects on police theorizing and research in the United States. In this article, we attempt to explain why this book failed to gain traction within the American policing literature. We argue that PRS was ignored for three reasons: (1) incommensurable theoretical frameworks, (2) timing and aim of the book’s publication, and (3) the intrusiveness of deductive surveillance technologies in the policing of identities. We conclude by discussing how Ericson and Haggerty’s theorizing should be revisited in the light of recent developments in 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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.086 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".