Dispositional malevolence and impression formation: Dark Tetrad associations with accuracy and positivity in first impressions
Bibliographic record
Abstract
OBJECTIVE: The Dark Tetrad traits (subclinical psychopathy, narcissism, Machiavellianism, and everyday sadism) have interpersonal consequences. At present, however, how these traits are associated with the accuracy and positivity of first impressions is not well understood. The present article addresses three primary questions. First, to what extent are perceiver levels of Dark Tetrad traits associated with differing levels of perceptive accuracy? Second, to what extent are target levels of Dark Tetrad traits associated with differing levels of expressive accuracy? Finally, to what extent can Dark Tetrad traits be differentiated when examining perceptions of and by others? METHOD: In a round-robin design, undergraduate participants (N = 412) in small groups engaged in brief, naturalistic, unstructured dyadic interactions before providing impressions of their partner. RESULTS: Dark Tetrad traits were associated with being viewed and viewing others less distinctively accurately and more negatively. CONCLUSIONS: Interpersonal perceptions that included an individual scoring highly on one of the Dark Tetrad traits differed in important ways from interactions among individuals with more benevolent personalities. Notably, despite the similarities between the Dark Tetrad, traits had unique associations with interpersonal perceptions.
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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".