Need for approval from others and face concerns as predictors of interpersonal conflict outcome in 29 cultural groups
Bibliographic record
Abstract
The extent to which culture moderates the effects of need for approval from others on a person's handling of interpersonal conflict was investigated. Students from 24 nations rated how they handled a recent interpersonal conflict, using measures derived from face-negotiation theory. Samples varied in the extent to which they were perceived as characterised by the cultural logics of dignity, honour, or face. It was hypothesised that the emphasis on harmony within face cultures would reduce the relevance of need for approval from others to face-negotiation concerns. Respondents rated their need for approval from others and how much they sought to preserve their own face and the face of the other party during the conflict. Need for approval was associated with concerns for both self-face and other-face. However, as predicted, the association between need for approval from others and concern for self-face was weaker where face logic was prevalent. Favourable conflict outcome was positively related to other-face and negatively related to self-face and to need for approval from others, but there were no significant interactions related to prevailing cultural logics. The results illustrate how particular face-threatening factors can moderate the distinctive face-concerns earlier found to characterise individualistic and collectivistic cultural groups.
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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 | 0.008 |
| 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 | 0.001 | 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".