Policing property and moral risk through promotions, anonymization and rewards: revisiting Crime Stoppers. Social and Legal Studies, 11(4
Bibliographic record
Abstract
This article explores promotions, anonymity and rewards as techniques of govern-ance in Canadian Crime Stoppers (CS) programmes by analysing texts and personal interviews. The function of CS Crime of the Week advertisements is found to be more a practical effort to reduce loss along property lines through offering rewards and anonymity and less a tactical effort to solve mostly violent crimes or a symbolic endeavour consistent with the promotion of ‘law and order ’ ideology. Through new partnerships with CS, various partners including private insurance gain symbolic but also practical risk management benefits. Anonymization promises to reduce risk to ‘tipsters ’ and moral risk to police and partners. A graduated system of rewards seeks to manage risk while encouraging risk among ‘tipsters ’ and is linked to moral imagin-ings of the tipster as ‘good citizen ’ and ‘criminal’. Risk and morality are therefore linked in this context. These techniques of governance are deployed together to render the policing of property and moral risks possible as these techniques are them-selves governed. CS does not simply aid law enforcement. Rather, in CS law is at once a way in which these techniques are governed and a barrier to their deployment. These findings have implications for the sociology of governance and law and move beyond previous research on CS.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".