Political Hearts of Darkness: The Dark Triad as Predictors of Political Orientations and Interest in Politics
Bibliographic record
Abstract
BACKGROUND: This study investigated the relationships between the Dark Triad of personality (sub-clinical psychopathy, Machiavellianism, and narcissism) and four political variables: socio-religious conservatism, support for greater economic equality, overall liberal-conservative orientation, and interest in politics. A theoretical approach that focused on the influence of the Dark Triad in large groups was provided to interpret those relationships. Methodological issues found in previous research that related to the use of abbreviated scales to measure the dark traits and the use of unidimensional indicators of political orientations were addressed. METHODS: A hierarchical regression analysis was conducted to determine whether any of the three dark traits could explain variance in the aforementioned political attributes over and above that accounted for by the Big Five, sex, age, and nationality, using the full personality scales and measures of political orientation that captured both social and economic liberalism-conservatism. RESULTS: Machiavellianism uniquely predicted lower levels of socio-religious conservatism, and both Machiavellianism and narcissism uniquely predicted lower levels of overall conservatism. CONCLUSIONS: There were important links between the Dark Triad and politics.
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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 | 0.003 |
| 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) | 0.004 | 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".