Psychopathic Traits and the Cheater–Hawk Hypothesis
Bibliographic record
Abstract
The present study was a direct test of the cheater-hawk hypothesis which argues that psychopathy is related to two potentially adaptive interpersonal strategies: cheating and aggression. As expected, the measures of cheater and hawk behaviors comprised a single factor, according to a maximum-likelihood factor analysis. As hypothesized, psychopathic traits exhibited large positive correlations with measures of both cheater (entitlement, exploitiveness, and short-term mating orientation) and hawk (vengeance and aggression) behaviors. Furthermore, psychopathic traits were associated with the tendency of using individualistic and competitive tactics in an altruism game and being less likely to act in a prosocial manner. Finally, scores on the combined-cheater hawk variable were significantly correlated with psychopathic traits. As hypothesized, individuals scoring high on Factor 1 of psychopathy were more likely to utilize behaviors and strategies associated with the cheater-hawk designation, whether or not they scored high on Factor 2 of psychopathy. In general, the findings support the conceptualization that psychopathy represents a fast life-history strategy characterized by seeking personal gain over others, including exploitiveness (cheater), aggression (hawk), and risk taking. Results also indicate that cheater and hawk behaviors are part of a single strategy more often employed by those higher on psychopathic traits. Implications for treatment success are discussed.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".