Testing the distinction between sadism and psychopathy: A metanalysis
Bibliographic record
Abstract
The relationships among the Dark Triad (DT) traits—Machiavellianism, narcissism, and psychopathy—are well-established in psychological literature. However, with the inclusion of everyday sadism in the proposed Dark Tetrad, it is important to determine whether sadism adds significant explanatory power beyond psychopathy, especially given its high correlation. In this study, we examined whether sadism contributed unique variance over psychopathy in studies where both traits were assessed. A review of PubMed, Google Scholar, and ScienceDirect yielded 185 studies meeting our inclusion criteria, comprising 104,452 participants. We analyzed sample characteristics, including type, size, gender distribution, age, and key correlates such as narcissism, Machiavellianism, the Big Five, and Honesty-Humility. Our results indicate a substantial overlap between sadism and psychopathy, with both traits being strongly related to the other DT traits and showing no correlation with Openness. These findings highlight the need for future research to account for this overlap when interpreting the relationships between sadism, psychopathy, and related psychological constructs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".