A Network Analysis Approach to Understanding Centrality and Overlap of 21 Dark Triad Items in Adults of 10 Countries
Bibliographic record
Abstract
Background: Previous research has suggested that manipulation and callousness are central to Dark Triad traits, but it has not identified which specific manifestations are expressed across various countries. Objective: This study aimed to identify the core and overlapping manifestations of Dark Triad traits across 10 countries. Methods: We used the Short Dark Triad (SD3) scale and assessed a sample of 8093 participants (59.7% women, M(age) = 32.68 years). For graphical representation, the spinglass algorithm was applied to understand the cluster distribution among Machiavellianism, psychopathy, and subclinical narcissism traits. Centrality indices were used to identify the most influential items, and the clique-percolation algorithm was employed to detect shared attributes among multiple Dark Triad items. Results: Straightforward SD3-21 items demonstrated better interpretability as aversive traits within the broader system. Items with higher centrality values were those related to short-term verbal manipulation from the psychopathy domain, clever manipulation, strategic revenge-seeking from Machiavellianism, and narcissistic motivations for connecting with significant individuals. The most predicted items were linked to planned revenge, using information against others from Machiavellianism, short-term psychopathic verbal manipulation, and narcissistic belief of specialness based on external validation. Items like short-term verbal manipulation had overlaps with both psychopathy and narcissism clusters, while clever manipulation overlapped with Machiavellianism and psychopathy. Conclusion: This cross-cultural study highlights the central role of verbal manipulation within the Dark Triad traits, along with identifying overlapping items among traits measured using straightforward SD3 scale items. In line with our findings, future research that incorporates a wide range of cultural contexts is encouraged to establish the consistency of these findings with the SD3 Scale or alternative measures.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".