Cognitive processing of moral and social judgements: A comparison of offenders, students, and control participants
Bibliographic record
Abstract
Examining cognitive processes related to offenders' moral and social judgements is important in order to better understand their criminal behaviour. In the present study, 30 offenders, 30 students, and 24 control participants were administered the moral-conventional judgements computer task, which requires responding under strict time constraints. Participants read scenarios and were asked to judge whether the act was acceptable or unacceptable when rules were either assumed or removed. Additionally, participants completed an executive function (EF) task in order to examine the relation between EF and moral and social judgements. The findings revealed that, as expected, controls and students had faster reaction times (RTs) and a higher percentage of normative judgements than offenders. Additionally, offenders had a low percentage of normative judgements, particularly in the conventional rule removed condition. Finally, RTs of moral and conventional judgements in most conditions were related to EF among students but not controls or offenders. We conclude that offenders, as compared to controls and students, may rely more on rule-oriented responding and may rely less on EF when making moral and social judgements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".