When flattery gets you nowhere: Discounting positive feedback as a relationship maintenance strategy.
Bibliographic record
Abstract
Intimates can rely on a number of strategies to protect their relationships from potential threats. In the present article, the authors investigate a new strategy: to discount flattering comments received from an attractive alternative to a dating partner by making a situational attribution. However, the authors did not expect everyone to adopt this strategy, as not everyone is likely sufficiently motivated to override both the tendencies to make dispositional attributions and to accept positive feedback from others. Dating and single participants were informed that an attractive alternative's positive impression of them had been made freely or under constraint. As expected, dating participants in the constraint condition were less likely than were those in the no-constraint condition to believe that the alternative's impression of them was genuine. In contrast, single participants believed that the confederate's impression of them was genuine, irrespective of their experimental condition. Self-esteem further moderated this effect. As hypothesised, only dating participants with low self-esteem were sufficiently motivated to recognise the situational constraint and discount the positive feedback. High self-esteem daters who were less inclined to discount the positive feedback instead protected their relationships by devaluing the alternative's attractiveness compared to singles.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".