Time-varying relational interaction dynamics in couples discussing conflict
Bibliographic record
Abstract
The process of discussing conflict can impact the quality and longevity of a couple’s relationship. Limited research has characterized the dynamic nature of conflict discussions, including how these discussions unfold and how an actor’s behaviour elicits particular responses from their partner over the course of the discussion. It is also important to consider how additional factors, such as alcohol intoxication, can influence this dynamic. A challenge in conducting this research is having sufficiently fine-grained data along with appropriate analytic methods to characterize the conversation dynamics. To address this gap, we utilized time-varying effect modeling (TVEM) to examine the correspondence of actor-partner behaviours as a function of time and alcohol consumption. We examined this using data from 139 heterosexual couples who were observed for 15 minutes while discussing a conflict. Couples were randomly assigned to have either one, both, or neither drink alcohol prior to the discussion. Using the Rapid Marital Interaction Coding System (RMICS), individuals’ behaviours were coded as either positive, negative, or neutral during each speaking turn. The results supported that positive behaviour tended to elicit positive behaviour and the strength of this relationship increased over the course of discussing conflict. While negative behaviours tended to elicit negative behaviours, the strength of this relationship was stable over time. Alcohol consumption did not alter the relations between actor-partner behaviours over time. Taken together, the findings support the dynamic nature of some aspects of actor-partner behaviours when discussing conflict. Future research could consider how these dynamics predict future relational outcomes or characterize how they unfold in naturalistic settings.
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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 | 0.004 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".