The experience of emotion in close relationships: Toward an integration of the emotion-in-relationships and interpersonal script models
Why this work is in the frame
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Bibliographic record
Abstract
We propose that the study of emotion in close relationships may be advanced through an integration of the emotion-in-relationships model (ERM) with interpersonal script models. In two studies, we tested the hypothesis that people experience emotion when expected patterns of relating are disrupted. We also predicted that the kinds of events that are perceived as disruptive, and the concomitant emotional response, would depend on the relationship context. The results indicated that emotional reactions do vary, depending on the type of relationship in which emotion is experienced. A key finding was that when an individual expresses dissatisfaction, a neglect response from a romantic partner is associated with more negative emotion than a neglect response from a friend. Implications of this finding are discussed. We conclude that interpersonal script models can be fruitfully incorporated into the ERM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it