Étude de certains déterminants des incendies volontaires à Montréal
Bibliographic record
Abstract
Research into the determining factors in arson cases has traditionally focused on factors linked to the characteristics of the burned building. One of our basic hypotheses is that deliberately set fires also have an underlying economic motivation. In this case, the present study confirms the hypothesis that there appears to be an indisputable link between the unemployment rate and mortgage burdens and arson rates, regardless of the phase of the economic cycle in which the arson occurs. Moreover, the study corroborates the idea that increased surveillance is necessary in areas presenting a higher risk of fraud and having a specific socioeconomic and financial profile. A lower incidence of arson and the improvement of insurers ' ability to predict losses due to arson could lead to a significant reduction in the number of claims, and consequently, in the amount of premiums. By looking more specifically at the economic motivations influencing arson throughout the different phases of the economic cycle, this study evokes the establisment of a forecasting system that would allow insurance companies to identify the areas of Montreal that present a higher risk level for arson, thus allowing them to establish their rates in a more equitable manner.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".