Bibliographic record
Abstract
Arson has economic, structural and psychological repercussions. As a crime with such wide- ranging consequences, it has received little academic attention. Our goal in this research is to highlight how arson can be understood from two perspectives: the anthropogenic environment and the physical environment. Study one employs a generalized linear mixed regression model to explore the relationship between street network permeability and the incidence of deliberately- set fire events in the City of Toronto. This research aims to highlight the important influence that navigation of the built environment has on crime, specifically arson, in addition to the social characteristics of place that support criminal behaviour. We hypothesize that neighbourhoods with more permeable (less complex) street networks are more likely to be affected by deliberately-set fire events in the case of Toronto. Also using a multivariate regression model, study two aims to highlight the role of heat aggression on the incidence of fire-setting behaviour in the same study region. We consider fire events occurring between the months of May through September, and particularly those occurring during extended heat-wave conditions. We hypothesize that prolonged episodes of high temperatures will have a positive relationship with arson events. This research highlights that two conceivably different forms of geography (anthropogenic and physical) can impact that same phenomena: criminal fire-setting behaviour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".