Firesetting Reoffending: A Meta-Analysis
Bibliographic record
Abstract
Despite the significant adverse consequences of deliberate firesetting, it has been unclear what proportion of individuals repeat this problematic behavior, owing to methodological differences and large variability in reported reoffending rates. A meta-analysis of 25 samples of untreated adults and children with a history of firesetting, examining reoffending over a follow-up period, was conducted. The base rates of reoffending from this meta-analysis indicated that between 57% and 66% of untreated firesetters engage in general reoffending, between 8% and 10% engage in criminal arson, and around 20% engage in deliberate firesetting behavior. The odds of firesetting during the follow-up period were 5 times greater for known firesetters in comparison with other offenders. Clinical and criminological correlates of reoffending, including age, are examined. Implications for enabling evidence-based practice with this population, including defensible risk assessments and treatment provision, are discussed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".