Accurate where it counts: Empathic accuracy on conflict and no-conflict days.
Bibliographic record
Abstract
When we are accurate regarding our partners' negative moods, are we seen as more responsive (and do we see them as such) as a function of the presence/absence of conflict? In 2 daily diary studies, empathic accuracy (EA) was assessed by comparing targets' daily negative moods with perceivers' inferences of these moods. We hypothesized that conflict will be associated with reductions in perceived partner responsiveness (PPR) for both parties; that on no-conflict days, EA will be positively associated with both parties' PPR; that on conflict days, this positive association will be stronger for targets but will become negative for perceivers; and that regardless of conflict, overestimation (vs. underestimation) of negative moods will be tied with higher PPR for targets but with lower PPR for perceivers. Thirty-six (Sample 1) and 77 (Sample 2) committed couples completed daily diaries (for 21 or 35 days, respectively). We utilized multilevel polynomial regression with response surface analyses, a sophisticated approach for studying multisource data of this sort (Edwards & Parry, 1993). Results partially supported our hypotheses: conflict was tied to reduced PPR; on no-conflict days, EA was not consistently predictive of target or perceiver PPR; on conflict days, EA predicted increased target PPR but decreased perceiver PPR; finally, overestimation predicted increased target PPR on no-conflict days and decreased perceiver PPR regardless of conflict. These results highlight the double-edged effects of EA on conflict days, and the importance of investigating dyadic EA in a context-sensitive approach. (PsycINFO Database Record
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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 | 0.023 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".