Longitudinal Associations Between Primary and Secondary Psychopathic Traits, Delinquency, and Current Dating Status in Adolescence
Bibliographic record
Abstract
Many have examined the desirability and mate competition tactics of adults higher on psychopathy using cross-sectional data, but few have studied the longitudinal associations between the lower-order factors of psychopathy (e.g., primary and secondary psychopathy) with indices of mating behavior in adolescents. More work is also needed to unravel how psychopathic youth outcompete rivals for mates. Delinquency has long been associated with dating and sexual behavior in adolescents, which may help to explain the competitive success of youth higher in psychopathic traits in vying for mates. We used cross-lagged panel modeling with three waves of data from a randomly drawn sample of 514 Canadian adolescents who provided annual self-reports of primary and secondary psychopathy, delinquency, and dating involvement from Grades 10 to 12 (15-18 years of age). Constructs were temporally stable. Secondary psychopathy and delinquency had positive within-time correlations with current dating status in Grade 10. A cross-lagged pathway from delinquency to dating involvement was supported from Grade 10 to 11, which replicated from Grade 11 to 12. However, this effect was specific to boys and not girls. An indirect effect also emerged whereby secondary psychopathy in Grade 10 increased the likelihood of being in a dating relationship in Grade 12 via heightened delinquency in Grade 11.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".