When consumers care about being treated fairly: The interaction of relationship norms and fairness norms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Prior research suggests that people assess overall fairness of an event by focusing on the distribution of the final outcome (distributive fairness) and on how they are treated by others during the conflict resolution process (interactional fairness). The primary goal of this work is to use a social relationship framework to study differences in consumers' responses to interactional fairness as revealed by their evaluations of a brand. Two types of relationships are examined—exchange relationships in which benefits are given to get something back in return; and communal relationships in which benefits are given to take care of others' needs. Results of two studies suggest that the type of consumers' relationship with the brand moderates the effect of interactional fairness such that consumers who have a communal relationship are more responsive to interactional fairness under conditions of low distributive fairness while those who have an exchange relationship are more responsive under conditions of high distributive fairness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it