The dark core and honesty-humility: (nearly) perfectly correlated yet distinct constructs. A proof by comparing their relations with self-reported revengefulness
Bibliographic record
Abstract
BACKGROUND: The traits constituting the Dark Triad (i.e., narcissism, Machiavellianism, and psychopathy) are expected to share a common dark core (i.e., antisocial attitudes towards others). However, there is an ongoing debate about whether the dark core is an independent construct or whether it falls within broader categories of personality (i.e., low honesty-humility). Previous research has been sceptical regarding the Dark Triad's incremental value as it is seen as redundant and adding little to traditional personality models. Thus, the current study aimed to assess the overlap and distinctiveness of the latent Dark Triad from honesty/humility. PARTICIPANTS AND PROCEDURE: = 3.42). Participants were recruited using social media and completed questionnaires anonymously through the LimeSurvey online platform. RESULTS: We replicated existing findings regarding the nearly perfect latent relationship between the dark core and honesty-humility using a broader array of measures of the Dark Triad traits. We also provided some evidence that the dark core and honesty-humility, despite being highly related, differ in terms of construct validity. CONCLUSIONS: Our findings suggest that claims positing complete convergence between these two constructs might be premature. However, future research examining different types of validity is needed.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".