The step-father effect in child abuse: Comparing discriminative parental solicitude and antisociality.
Bibliographic record
Abstract
Objective: The greater risk of abusing a step-child than a genetically related child has been attributed to discriminative parental solicitude. We tested whether it is better explained by antisociality, whereby more antisocial fathers are more likely both to have step-children and to be generally more violent. Method: We studied police reports of assaults on children by 387 domestically violent men who had a minor child, and their bivariate association with genetic relatedness, offender antisociality, and opportunity to assault step-children. In the subsample of 118 men with the opportunity to assault both step and genetically related children, we tested whether fathers were more likely to assault step-children, overall and among more antisocial men. Results: Number of step-children was associated with both child abuse and 2 of 3 measures of antisociality. When opportunity was controlled, fathers showed evidence of discriminative parental solicitude, being twice as likely to assault step-children as genetically related children. This step-father effect was observed at all levels of antisociality. Conclusion: Antisociality alone cannot explain the step-father effect. Discriminative parental solicitude remains a viable explanation for the step-father effect observed in this study. Research is needed to explore more proximal causes of the step-father effect.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".