The origins of darkness: An evolutionary-developmental integration of Dark traits with the HEXACO
Bibliographic record
Abstract
Exploitative, selfish behavior has been a topic of interest to researchers across a wide range of domains. One prominent area of research has focused on personality as an important contributor to harmful and selfish behavior. The term “Dark Triad” was coined to identify three selfish personality traits that all share a common core (D): Narcissism, Machiavellianism, and Psychopathy. While numerous candidates have emerged to explain D, evidence suggests that the HEXACO model of personality provides the best fit. In particular, the latent core of the HEXACO trait Honesty-Humility (H) appears to be statistically identical to the latent core of D ( r ≈ 0.95). We adopt an evolutionary-developmental perspective to address how this common core may have emerged over evolution as well as developed in individuals. Specifically, we offer: 1) an adaptive explanation of high and low levels of H/D, 2) a parsimonious and plausible evolutionary and developmental model to explain the existence of Dark Triad traits that fits with both modern and historical patterns of human behavior, and 3) an explanation for how developmental and evolutionary processes of modification can change the levels of other HEXACO traits to lead to the expression of not only the Dark Triad, but to an entire array of Dark traits. We end by discussing implications for intervention and understanding personality models of exploitative, selfish traits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".