Responses to dissatisfaction in friendships and romantic relationships: An interpersonal script analysis
Bibliographic record
Abstract
According to interpersonal script models, people’s responses to relational events are shaped by the reaction they expect from a close other. We analyzed responses to dissatisfaction in close relationships from an interpersonal script perspective. Participants reported on how a close friend or romantic partner would react to their expressions of dissatisfaction (using the exit-voice-loyalty-neglect typology). They were also asked to forecast whether the issue would be resolved (i.e., anticipated outcomes). Our main hypothesis was that people’s expectations for how a close other would respond to dissatisfaction would be dependent on their own self response. Further, we predicted that passive responses would be more common and viewed as less deleterious to a friendship than a romantic relationship. Results indicated that the responses that were expected from close others were contingent on how self responded. Moreover, as predicted, these contingencies followed different tracks depending on the type of relationship. Friends were more likely to expect passive responses to self’s expression of dissatisfaction, especially if self responded with neglect, whereas romantic partners expected more active responses. Furthermore, people anticipated that the issue would be more likely to be resolved if their friend (vs. romantic partner) responded passively and less actively (especially for destructive responses). It was concluded that people hold complex, nuanced interpersonal scripts for dissatisfaction and that these scripts vary, depending on the relationship context.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".