Making and maintaining relationships through the prism of the dark triad traits: A longitudinal social network study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: We investigated how Dark Triad traits influence the development and maintenance of social relations. METHOD: Participants completed the Short Dark Triad questionnaire and a measure of social relations at three time points: at the beginning of their first year in high school, 3 months later, and at the end of their first year. We investigated whether the Dark Triad traits are stable over time using Multilevel Modeling (N = 265; 59.6% girls), and how Dark Triad traits predict incoming and outgoing agentic and communal relations using Temporal Exponential Random Graph Models (N = 192; 60.4% girls). RESULTS: Overall, the Dark Triad traits were stable over a one-year period. Narcissism did not predict an increase in communal and agentic relations in the short-term, but predicted slightly less incoming communal and more agentic relations in the long-term. In the short-term, Machiavellianism predicted a small increase while psychopathy predicted a small decrease in the incoming agentic and communal relations. In the long-term, however, neither Machiavellianism nor psychopathy was a significant predictor of any incoming relations. CONCLUSIONS: Our results shed new light on the dynamics of making and maintaining social relations through the prism of the Dark Triad traits.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it