Responses to verifying and enhancing appraisals from romantic partners: the role of trait importance and trait visibility
Bibliographic record
Abstract
Abstract An experiment assessed when people respond more positively to verifying and enhancing appraisals from romantic partners. Two‐hundred and fifty‐eight individuals comprising 129 dating couples participated in this research. Couples privately rated their self‐concept on traits that were either high or low on trait visibility, rated how important each trait was to them, and rated their partners. A computer program ostensibly compared their self‐ratings with appraisals from their partners on traits they selected as being high or low in personal importance, and participants received either verifying or enhancing feedback. Confirming predictions, people believed their partners understood them more when they received verifying feedback, but felt their partners saw the best in them when they received enhancing feedback. Additionally, people responded more positively to verifying appraisals on important, less visible traits, and enhancing appraisals on important, highly visible traits. Results are discussed in terms of preferences for enhancing and verifying feedback in romantic relationships. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.016 |
| 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 | 0.000 |
| 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".