A modified Stroop task with sexual offenders: Replication of a study
Bibliographic record
Abstract
Cognitive–behavioural treatment of sexual offenders assumes that sexual offenders are motivated by deviant attitudes, perceptions and values. Although aspects of deviant schema can be assessed by questionnaires, self-report measures are limited by the respondent's willingness to be forthright and by the fact that, typically, these cognitive processes occur quickly, revealing signs of automaticity. Recent research by Smith and Waterman has suggested that the deviant schema of sexual offenders could be assessed using a version of the Stroop colour-naming task. Long latency periods to sexual colour words imply a longer information-processing route and evidence of pre-established (deviant) sexual cognitive schema. Stroop techniques may offer the advantage of eliminating limitations that arise when using self-report techniques, such as fakeability and social desirability concerns. The current study replicates and extends Smith and Waterman's results using samples of sexual offenders, non-sexual violent offenders and non-violent offenders. The cumulative results of the two studies suggests that Stroop techniques have promise, but that further work is required before measures are available that have sufficient reliability and validity to be used in applied contexts.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".