Discount and disengage: How chronic negative expressivity undermines partner responsiveness to negative disclosures.
Bibliographic record
Abstract
Partner responsiveness-the degree to which partners respond with caring, understanding, and validation to one another's disclosures (Reis, Clark, & Holmes, 2004; Reis & Shaver, 1988)--has been heralded as a "core, defining construct" in relationship science (Reis, 2007, p. 28). Yet little is known about the determinants of responsiveness in ongoing relationships. The present research elucidates one such set of processes, focusing in particular on responsiveness to negative disclosures. We predicted that the degree to which a partner behaves responsively to negative disclosures depends on the partner's perception of the discloser's typical expressive tendencies. Results of 5 studies employing both correlational and experimental methods supported the hypothesis that partners are less responsive to negative disclosures made by disclosers whom they perceive to have high negativity baselines-that is, to express negativity frequently--than to identical (Studies 1-4) or equally negative (Study 5) disclosures made by disclosers with lower negativity baselines. We also examined 2 routes through which negativity baselines might affect partner responsiveness: by shaping listener appraisals of the discloser's need for support and by making disengagement from those interactions seem justifiable to listeners. These findings fill an important gap in the responsiveness literature and highlight the utility of considering person-context factors in emotion interpretation and responsiveness processes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".