Commentary: Models and correlates of firesetting behavior.
Bibliographic record
Abstract
From antiquity, fire has played an integral role in human survival. Numerous references to its mythical and religious significance can be found in ancient biblical and mythological texts.1–4 In modern times, however, fire has been less a symbol of reverence and too often a tool of violence and destruction.2 Every year in the United States, approximately 500,000 incendiary and suspect fires occur, causing over $2 billion in losses, 3,500 injuries, and 750 deaths.5 Ac-cording to Lyman,6 “when measured on a cost per incident basis, arson is the most expensive crime committed.” No other area of forensic practice has been more detrimentally affected by inaccurate presumption than firesetting. Fineman7 contends that such wide-spread misunderstanding is largely due to the failure of mental health professionals to dispel misconcep-tions about fire-related behavior. Clearly, it is incum-bent on mental health professionals to work with investigators and professionals across disciplines in an effort to facilitate a better understanding of fire-setting phenomena. The paper that we have been asked to comment on is potentially an important source of data on this complex behavior.8 We will begin by reviewing the literature on juvenile fireset-ting to place this article in the context of present knowledge.
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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 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.041 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".