Propensity to Morally Disengage Scale: Psychometric Properties and Measurement Invariance Among a Portuguese Sample
Bibliographic record
Abstract
The propensity to morally disengage can be an essential driver of unethical, antisocial, and criminal behavior. The present study examines the psychometric properties of the Propensity to Morally Disengage Scale (PMDS) among a convenience sample of 242 male and female participants (M = 30.19 years, SD = 12.78, range = 16–77) from Portugal. The expected one-factor structure obtained an adequate fit using confirmatory factor analysis. Internal consistency/reliability was adequate as measured by the alpha and omega coefficients. Convergent validity (i.e. with dark traits, low self-control, violence evaluation, and antisociality/criminality tendencies measures), divergent validity (i.e. with basic empathy and light traits of personality measures), and criterion-related validity (e.g. with trouble with the law, arrested by police, sentenced to prison variables) were demonstrated with Pearson and point-biserial correlations. Measurement invariance across gender was established. Significant gender differences in the PMDS scores were found, with males scoring significantly higher than females. Our findings support using the PMDS Portuguese version as a short, valid, and reliable measure of moral disengagement.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".