Properties of the Continuous Assessment of Interpersonal Dynamics Across Sex, Level of Familiarity, and Interpersonal Conflict
Bibliographic record
Abstract
The Continuous Assessment of Interpersonal Dynamics (CAID) is a method in which trained observers continuously code the dominance and warmth of individuals who interact with one another in dyads. This method has significant promise for assessing dynamic interpersonal processes. The purpose of this study was to examine the impact of individual sex, dyadic familiarity, and situational conflict on patterns of interpersonal warmth, dominance, and complementarity as assessed via CAID. We used six samples with 603 dyads, including two samples of unacquainted mixed-sex undergraduates interacting in a collaborative task, two samples of couples interacting in both collaborative and conflict tasks, and two samples of mothers and children interacting in both collaborative and conflict tasks. Complementarity effects were robust across all samples, and individuals tended to be relatively warm and dominant. Results from multilevel models indicated that women were slightly warmer than men, whereas there were no sex differences in dominance. Unfamiliar dyads and dyads interacting in more collaborative tasks were relatively warmer, more submissive, and more complementary on warmth but less complementary on dominance. These findings speak to the utility of the CAID method for assessing interpersonal dynamics and provide norms for researchers who use the method for different types of samples and applications.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".