Crime radiation theory: the co-production of crime patterns through opportunity creation and exploitation
Bibliographic record
Abstract
Abstract Considerable research shows that crime is concentrated at a few proprietary places: addresses and facilities. Emerging research suggests that proprietary places may radiate crime: activities at a place increase the risk of crime in the area around it. Weaknesses in the research create uncertainty about radiation, so we need more rigorous research. To conduct this research, we need a theory of crime radiation that operates at two spatial levels: the proprietary place and the area. This paper describes such a theory. Our theory states that crime radiation stems from the interaction between place management decisions at the place and offenders searching for opportunities in the area. Place managers create crime opportunities inside and outside their places. Offenders exploit place managers’ creations by deliberately searching for opportunities or by chancing upon the opportunities. The ways place managers and offenders interact gives rise to three types of crime radiation: hot dot, veiled dot, and cold dot. Finally, we propose questions crime scientists should answer to better understand crime radiation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".