Defer, disagree, or disengage? Relational mobility, self-changeability, culture, and responses to inconsistency
Bibliographic record
Abstract
Past research has found that people often engage in one of three behaviours to resolve inconsistencies with others: validation, conforming, or distancing. While much of the social influence literature has assessed contexts in which conformity is more or less likely, little has explored alternatives to conformity, such as validation and distancing. The present research as aims to assess what underlying factors that affect these preferences. We used vignettes of inconsistency in relational contexts to assess people’s preferred responses. In Study 1, we assessed Canadian participants’ preferences responses following social inconsistency. We found that more perceived self-changeability increased preference for conformity and decreased preference for validation and distancing, while more perceived relational mobility increased preference for distancing and decreased preference for conformity and validation. In Study 2 we assessed how these behaviours may vary culturally. We found that compared to Canadians, Koreans expressed greater preference for conformity and less preference for validation. The patterns of results for perceived changeability and mobility were largely the same across Canadian and Korean, with Koreans demonstrating similar or smaller effects. Implications and limitations of these findings are discussed.
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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 | 0.006 | 0.030 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".