Dating Aggression in Emerging Adulthood: Interactions Between Relationship Processes and Individual Vulnerabilities
Bibliographic record
Abstract
The current study examined the roles of relationship processes and individual vulnerabilities in predicting dating aggression perpetration during emerging adulthood. Drawing from the contextual-situational model of courtship aggression (CSM; Riggs & O'Leary, 1989), as well as other theoretical models of close relationships, we hypothesized that individuals' depressive symptoms and attitudes condoning aggression would moderate the link between the perceived relationship bond and partner aggression perpetration. Using a multi-method, multiinformant approach with college dating couples, we found that highly aggressive couples (n = 23) differed from moderately (n = 27) and nonaggressive couples (n = 15) in having lower perceived relationship bonds, lower female relationship satisfaction, more female depression, and higher male attitudes condoning aggression. Among the 50 physically aggressive couples, a lower perceived relationship bond interacted with symptoms of depression to predict higher levels of psychological and physical aggression perpetration, and higher attitudes condoning aggression further exacerbated the risk for men's physical aggression perpetration only. These associations remained even after controlling for self-reported relationship satisfaction and aggression victimization. Findings from this study are consistent with the CSM and suggest that a lower perceived relationship bond, particularly in combination with symptoms of depression and attitudes condoning aggression, place men and women at increased risk for dating aggression perpetration during emerging adulthood.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".