Authority fairness for all? Intergroup status and expectations of procedural justice and resource distribution
Bibliographic record
Abstract
Authorities such as the police and the government play a vital function in maintaining order in the social systems in which groups exist. Relational models of procedural justice (PJ) state that fair treatment from authority affirms the social standing of those identifying with the authority, communicating inclusion and respect. Previous research suggests that social identity may also inform expectations of authority fairness. Focusing on an intergroup context of authority decision-making, the present research tests a novel hypothesis regarding whether intergroup social status may also inform expectations of authority fairness in terms of fair treatment and favourable outcomes. Operationalising PJ as the extent to which people are provided voice by authorities, three experimental studies showed no effect of intergroup status on expected PJ from authority. A sample weighed internal meta-analysis (N = 704) also provided no support for the hypothesis that relative outgroup status shapes expectations of voice from authority (d = -.02). Intergroup status did, however, influence the extent to which people expected authorities to distribute resources favourably towards the outgroups. Lower status outgroups were expected to receive less favourable outcomes from authorities than equal status outgroups (d = -.23). Thus, outgroup status affects people’s judgements of the resources that outgroups deserve from authority. The present research is among the first to consider how intergroup relations may drive expectations of how authorities will act towards other social groups. Implications for wielding authority and the role of perceived intergroup threat in intergroup settings 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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".