Distributive and Procedural Justice for Self and Others
Bibliographic record
Abstract
Tendencies to believe in justice are multidimensional, and some justice beliefs enhance personal well-being. These features suggest a considerable but largely overlooked potential for similarities and differences in the structure, endorsement, and wellness-promoting functions of justice beliefs across cultures. In the current research, we evaluate a recently available four-factor conceptualization of justice beliefs in samples of university students from the United States, Canada, India, and China (total N = 922). Multigroup confirmatory factor analysis demonstrated that the proposed four-factor model was structurally invariant, suggesting that individuals from all four cultures could be characterized according to their beliefs about distributive and procedural justice for both self and others. Cross-cultural comparisons revealed no mean differences in beliefs about distributive justice for self, whereas beliefs about procedural justice for self were higher in Canada and China than in the United States or India. In parallel, beliefs about distributive and procedural justice for others were higher in Eastern than in Western cultures. In all four cultures, a belief in distributive justice for self was associated with greater life satisfaction, whereas a belief in procedural justice for self was additionally associated in Canada and China only. No associations between beliefs about justice for others and life satisfaction were observed in any culture. The current research provides initial support for the cross-cultural viability of a four-factor approach to measuring dispositional tendencies to believe in justice. We discuss implications and opportunities for the continued study of justice in cross-cultural research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| 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".