The pictorial Fire Stroop: a measure of youthful fire interest?
Bibliographic record
Abstract
Childhood interest in fire, and the involvement with fire that often derives from this interest, are critical clinical issues that can have serious implications for society as a whole. It is, therefore, of considerable import that we have effective tools for identifying and measuring youthful fire interest. The emotional Stroop task is an experimental paradigm that has proven to be effective at measuring the information-processing biases of individuals drawn from diverse clinical and nonclinical populations. The purpose of the present study was to test whether a computer-based fire-specific emotional Stroop task, as well as associated Stroop-stimulus recognition and interest rating tasks, can be used effectively to measure an information-processing bias for fire-related stimuli (operationally defined here as fire interest). Clinic-referred and nonreferred adolescents (aged 13-16 years) first completed a practice task followed by a pictorial "Fire Stroop"—an emotional Stroop task that involves the presentation of neutral and fire-related pictures. Participants then completed a Stroop Picture Recognition task, a Stroop Picture Level-of-Interest Rating task, a standard Stroop color-word task, the child version of the TAPP-C Fire Interest Questionnaire (FIQ-C), and a standardized test of verbal and visual comprehension. Results showed that the Fire Stroop is unique in its effectiveness as an information-processing measure of juvenile interest in fire. The findings also indicated that fire interest, as inferred from the Fire Stroop, is greater among adolescents referred for firesetting than it is among clinic-referred and nonreferred controls. Additionally, study findings revealed there to be a lack of correspondence between the Fire Stroop's information-processing indices of fire interest and a self-report index of fire interest derived from the FIQ-C. This result was taken as indication not just of the shortcomings of self-report, but also of the Fire Stroop's potential as an objective method for tapping into youthful interest in fire. Collectively, these findings point to the viability of alternative methods for measuring fire interest. These results also contribute further to the conceptualization of juvenile firesetters, draw attention to the prevalence of youthful interest in fire, and argue for more intervention programs that will address fire interest and fire involvement among youths.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".