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
Abstract Purpose The introduction of community policing led to a significant increase in the number of police stations, particularly in urban settings. Police stations are largely assumed to have an impact on crime but there are few studies dedicated to the issue. Methods The concept of deterrence suggests a negative relationship between police and crime: an increased police presence should lead to a reduction of crime. While it is difficult to directly test that relationship, the present study takes advantage of two recent events in Montreal (Canada) to test the hypothesis that the closure of a police station causes an increase of crime in the surrounding area. Andresen’s Spatial point pattern tests and Wheeler and Ratcliffe’ weight displacement difference tests were conducted. Findings While tests suggest that crime geographic patterns were dissimilar pre- and post-closure, none of those differences support the deterrence hypothesis because the number of areas in which an increase in crime was recorded is lower than would be expected by chance. Similarly, decreases in breaking and entering, mischief, theft in or on vehicles and total crime were found, which does not support the deterrence hypothesis. Conclusions The study of hotspot policing led to the belief that police presence needs to be concentrated in both time and space if it is to have a significant preventive impact on crime. It also led to the development of strategies of concentrated policing that encompass a variety of prevention actions aimed at specific individuals, specific crime types, and/or specific areas. Police stations provide something different: a concentrated presence at one point location with the ability to deploy to respond to any crime, at any time, in a particular area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it