Exploring ‘near’: Characterizing the spatial extent of drinking place influence on crime
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The important role of facilities in understanding crime patterns is widely recognized. Studies have demonstrated a connection between the presence of facilities such as bars, parks, schools and fast food restaurants and higher crime rates. Typically, these studies use a single distance threshold. Areas within the threshold are assumed to be related to the facility and those outside the threshold unrelated. But the choice of threshold in each study is usually an ad hoc decision based on the expertise of the researcher. Until recently, there has been no systematic evaluation of the methodology used to define those thresholds. This paper evaluates two methods for determining an empirically-based answer to the question ‘How close is “near”?’ The results of an example analysis testing the association of drinking places and crime in Seattle, Washington are reported. The two most common facility-based measures, Euclidean distance buffers and Street distance buffers are compared across two levels of aggregation and 18 separate distances. Findings indicate the geographic extent of increased crime around drinking places varies based on the type of buffer (Euclidean vs. Street distance) and the width of the distance bands (street block vs. 402 meter (quarter mile) increments). The geographic extent of the influence of drinking places on crime is best captured by street distance measures across street block distances (122 meter bands). Implications of these findings for theory and practice are discussed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it