Bibliographic record
Abstract
Abstract Research summary Inspired by studies on crime concentration, scholars have begun examining the spatial patterns of other issues under the police mandate, such as calls for service involving persons with perceived mental illness (PwPMI). While findings show that PwPMI calls for service concentrate in a few number of places, we do not know whether the concentration of these calls fall within a narrow bandwidth of spatial units nor whether these calls are spatially stable. Drawing on 7 years of calls for service data from the Barrie Police Service, this study tests for the temporal stability of PwPMI call for service concentrations at two units of spatial analysis and applies a longitudinal variation of the Spatial Point Pattern Test to assess the spatial stability of these calls at both the global and local levels. The results reveal that concentrations of PwPMI calls for service not only fall within a narrow proportional bandwidth of spatial units, but are spatially stable, even during the COVID‐19 pandemic. Policy implications Existing police‐ and community‐based efforts that respond to PwPMI in the community are tasked with responding to crises that could have been prevented with timelier intervention. Drawing from crime‐focused, place‐based policing strategies whose deployment is informed by the spatial concentration of crime, scholars have similarly argued that knowledge on where PwPMI calls for service concentrate can be leveraged to inform and deploy place‐based efforts whose focus is to assist PwPMI in a proactive capacity. The findings of the present study further substantiates the deployment of PwPMI‐focused police‐ and community‐based resources as proactive, place‐based efforts. In doing so, these efforts could not only prevent mental health crises from occurring but could prevent future police‐involved calls for service and thus reduce the footprint of the police in the lives of PwPMI in a reactive capacity.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".