Bibliographic record
Abstract
Objectives: Criminal target choice has been described as a multistage process: An offender first selects a suitable area from a set of alternatives and then chooses a specific target. This article studies area selection and attempts to distinguish between crime generators/visit detractors (elements that could affect anyone) and crime attractors/offense detractors (elements that affect offenders specifically). Methods: Trips that resulted in violent or property crimes between 506 census tracts in a large city ( n = 11,411) are analyzed. Multilevel negative binomial regression is used to assess the impact of measures relating to pairs of tracts and characteristics of destination tracts. Results: Various factors are significantly related to the number of crime-associated trips per pair of tracts: differences in reward (residential and visiting population size, presence of schools or bars), differences in effort (distance between tracts, major roads linking both tracts), and differences in risk (level of social disorganization). Conclusions: This article supports an “opportunistic perspective” on crime: Crime-associated trips are more likely when advantages are high and risks and efforts are low.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".