Enthusiastic Acts of Evil: The Assessment of Sadistic Personality in Polish and Italian Populations
Bibliographic record
Abstract
Subclinical sadism has received substantial attention in recent research as a trait that predicts a variety of malevolent behaviors. The objective of this study was to assess the 'psychometric robustness and portability' of the Assessment of Sadistic Personality (ASP). We examined the convergent and discriminant validity, and invariance of translated versions of the ASP within community samples of Polish and Italian individuals. The study included 568 individuals (340 women and 228 men) residing in Italy (Mage = 23.57, SDage = 2.55) and 556 individuals (411 women, 144 men, 1 other) residing in Poland (Mage = 23.48, SDage = 4.60). For cultural invariance purposes, data from a Canadian sample comprising 638 students were used. To establish convergent and discriminant validity, participants completed measures of sadism, the Dark Triad, the Big Five, interpersonal reactivity, and maladaptive traits described in the DSM-5. Across both samples, convergent and discriminant validity were supported. Configural and partial metric invariance were satisfied, and following implementation of alignment optimization, latent mean differences were evaluated between countries. Results of the study supported the psychometric qualities of the ASP across different cultures and languages, and the utility of the ASP as a valid measure extending beyond university samples.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".