The Law of Crime Concentration at Places: Editors’ Introduction
Bibliographic record
Abstract
The empirical observation that a small number of micro places generate the bulk of urban crime problems has become a criminological axiom. These micro places, commonly referred to as “hot spots” of crime, have been defined as clusters of street addresses, groupings of street blocks, and particular street intersections and street segments (Weisburd et al. 2009). High-activity crime places have been found in neighborhoods characterized by both low and high levels of social disadvantage (Weisburd and Green 1995; Curman et al. 2015). More recently, researchers have also demonstrated that the concentration of crime at particular places is stable over time (Weisburd et al. 2004; Braga et al. 2010; Andresen and Malleson 2011). The stability of crime at hot spots reinforces the logic that underlies place-based prevention strategies. Without intervention, crime hot spots are not likely to disappear in short time periods. Concentrating crime prevention resources on such places makes good sense both for short term and long term gains (Braga and Weisburd 2010; Weisburd et al. 2004).
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".