An Empirically Derived Classification System for Juvenile Firesetters
Bibliographic record
Abstract
Despite the heterogeneity of juvenile firesetters, the literature lacks empirically based classification systems. Existing typologies have been descriptive, arbitrarily segregate subtypes based on univariate characteristics, and lack empirical validation. In the present investigation, cluster analysis was used to develop a classification of juvenile firesetters based on both fire-specific and general individual and environmental variables associated to firesetting severity and recidivism. Participants included 240 firesetters aged 4 to 17 and primary caretakers who were referred to The Arson Prevention Program for Children. Findings indicate that juvenile firesetters are a heterogeneous group that can be empirically separated into conventional-limited, home-instability-moderate, and multi-risk-persistent firesetters. These subtypes differ on fire-specific characteristics, individual and environmental variables, and firesetting recidivism and general outcome. The implications of this classification system for conducting fire-risk assessments and implementing prevention and treatment strategies are also 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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".