Conflict in Love: An Examination of the Role of Dark Triad Traits in Romantic Relationships among Women
Bibliographic record
Abstract
The present study examined how the personality dimensions of the Dark Triad (i.e., Machiavellianism, narcissism, and psychopathy) predict infidelity intentions and jealousy and whether these variables predict conflict tactics used in relationships. Adult women (N = 567, 18–73 years old, Mage = 31.91; SD = 10.29) completed self-report scales assessing the Dark Triad traits, jealousy (i.e., cognitive, emotional, and behavioral), intentions towards infidelity, and conflict tactics, including negotiation, psychological aggression, physical assault, sexual coercion, and injury. Our results demonstrated that the Dark Triad traits had strong links to the intention to commit infidelity and jealousy, and at the correlational level, there were small correlations between jealousy and the intention to commit infidelity. Both jealousy and the intention to commit infidelity predicted conflict tactics. As this is possibly one of the first studies to examine these variables jointly, the present results add to our understanding of the role of personality in romantic relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".