Inconsistencies in Associations Between Crime and Walking: A Reflection of Poverty and Density
Bibliographic record
Abstract
Higher crime rates theoretically deter walking, yet empirical analyses show mixed results. The authors hypothesized that high walking rates occurs in high density and lower income areas that tend to have higher levels of crime. Furthermore, gender, car ownership and relative wealth may potentially moderate associations between crime and walking. In an attempt to disentangle these effects, a statewide New Jersey survey (n= 673) of walking and perceptions of the social and built environment were linked to crime and census data. The authors identified municipal correlates of violent crime rates and used a sequential modeling approach to estimate walking. Women were more likely to walk for exercise, but less likely as crime rose. Carless households and wealthier respondents were positively associated with non-discretionary walking, but walked less when crime rates were higher. High density, poorer municipalities have higher crime rates and also have more walking, explaining discrepancies in results of previous studies.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".