Culture and Procedural Fairness: When the Effects of What You Do Depend on How You Do it
Bibliographic record
Abstract
Previous research has shown that procedural fairness and outcome favorability interactively combine to influence people's reactions to their social exchanges. The tendency for people to respond more positively when outcomes are more favorable is reduced when procedural fairness (how things happen) is relatively high. This paper evaluates whether cultural differences in people's tendencies to view themselves as interdependent or independent (their self-construal) moderate the interactive relationship between procedural fairness and outcome favorability. In three studies, participants indicated their reactions to an exchange with another party as a function of the other party's procedural fairness and the outcome favorability associated with the exchange. In Study 1, participants' national culture was treated as a proxy for their self-construal. In Study 2, people's national culture and self-construal were assessed. In Study 3, participants were classified on the basis of their self-construals. Converging evidence across studies showed that the interactive relationship between procedural fairness and outcome favorability was more pronounced among participants with more interdependent forms of self-construal.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".