Overview of: “Crime Place and Pollution: Expanding Crime Reduction Options Through a Regulatory Approach”
Bibliographic record
Abstract
Research Summary Crime reduction policy has focused almost exclusively on offenders. Recent studies and evaluations show that expanding our policy portfolio to include places may be highly productive. We show that there is considerable research showing that crime is concentrated at a relatively few locations, that high‐crime places are stable, that changing places can reduce crime, that displacement is not only far from inevitable but also less likely than the diffusion of crime prevention benefits, and that owners of high‐crime places can be held accountable for the criminogenic conditions of their locations. We link these findings to environmental policy, where environmental scientists, economists, and regulators have developed a broad set of regulatory options. The core of this article describes a portfolio of environmental policy instruments directly applicable to crime places. We also discuss major decisions local governments will need to make to implement various forms of regulation, and we list challenges that governments must anticipate in planning for such implementation. We argue that a regulatory approach to crime places has the potential to lower the cost to taxpayers of reducing crime by shifting costs from governments to the relatively few place owners whose actions create crime‐facilitating conditions. Policy Implications Taking a regulatory approach to crime places substantially expands the crime policy options under consideration. Regulatory options may increase local governments’ effectiveness at reducing crime while reducing governments’ costs. This is because regulatory approaches have the potential to shift some portion of the financial burden for crime fighting to owners of criminogenic locations. Policy makers can select between means‐based anticrime regulations that focus on how place owners manage their locations and ends‐based regulations that focus on the number of crimes allowed at places. Both of these approaches contain several alternative regulatory instruments, each with its own set of advantages and disadvantages. Experimenting with various regulatory instruments could lead to the development of a range of new crime reduction policies. In addition, a regulatory approach has implications for the funding of policy research. Means‐based regulatory instruments require governments to develop evidence that the means they regulate have the desired impact on crime. Ends‐based regulatory instruments shift this burden to the regulated places.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".