Why might psychopathy develop? Beyond a protective function: a commentary on Zara <i>et al.</i> (2023)
Bibliographic record
Abstract
Purpose Zara et al. (2023) provide novel findings into how psychopathy may develop, showing that early life predictors of poor relationships (e.g. being unwanted before birth) are predictive of psychopathy in adulthood. The authors provide a theoretical interpretation of why psychopathy might develop based on these findings by using an adaptive perspective, suggesting that psychopathy may protect or shield individuals from poor relationships. This commentary aims to critically evaluate and extend this latter suggestion in hopes of fostering further research and clarity on the topic. Design/methodology/approach After presenting an overview of evolutionary perspectives, a summary and elaboration are presented of the interpretation that psychopathy may be an adaptive response that functions to protect individuals from poor relationships. Then, an additional adaptive interpretation is offered. Findings Psychopathy describes a collection of traits and behavior that facilitates an approach-oriented and exploitative motivational style that might suggest more than a protective function. When negative or poor relationships are experienced (e.g. being unwanted), it is suggested that psychopathy may begin to develop not just for protection (If I am not loved, I will shield myself from those around me) but to actively orient toward exploitation (If I am not loved, I will exploit those around me). Originality/value This commentary hopes to arouse further interest into the theoretical interpretations of why psychopathy may develop that are based on findings of how psychopathy develops. These considerations are consequential for understanding what to target in treatments that aim to meet the specific needs and motivations of individuals with psychopathic 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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.045 | 0.046 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".