Dating Aggression and Observed Behaviors in a Nonconflictual Situation: The Role of Negative Anticipation
Bibliographic record
Abstract
Past observational studies highlight meaningful behavioral differences between aggressive and nonaggressive couples during conflict interactions. However, research is needed on how aggressive couples communicate in other, nonconflictual interactional contexts. This study investigates how dating partners’ perpetration of physical aggression relates to observed behaviors during a laboratory-based discussion during which dating couples planned a date together. We also investigated whether negative anticipation of the upcoming discussion influences dating partners’ observed behaviors. Results showed that perpetration of dating aggression from one partner is linked to more negative behaviors from the other partner during the discussion. This association, however, is moderated by negative anticipation of the discussion; the link between aggression from one’s partner and negative behaviors is significant at high levels (+1 SD) but not at low levels (–1 SD)of negative anticipation. One’s own dating aggression also relates to fewer positive behaviors during the discussion. Findings suggest that couple aggression spills over to and potentially degrades the discussion of even nonthreatening, potentially enjoyable communications. Results also underscore negative anticipation of an interaction as a potential risky process that increases the likelihood of antagonistic exchanges between partners. The discussion addresses putative pathways between partner aggression and generalized communication patterns, and potential bi-directional effects with negative anticipation. We also discuss practical implications and targets of intervention to counteract the establishment of problematic communication dynamics in young couples.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".