Stability and the justification of social inequality
Bibliographic record
Abstract
Abstract Modern society is rife with inequality. People's interpretations of these inequalities, however, vary considerably: Different people can interpret, for example, the existing gender gap in wages as being the result of systemic discrimination, or as being the fair and natural result of genuine differences between men and women. Here, we examine one factor that may help explain differing interpretations of existing social inequalities: perceptions of system stability. System justification theory proposes that people are often motivated to rationalize and justify the systems within which they operate, legitimizing whatever social inequalities are present within them. We draw on theories and evidence of rationalization more broadly to predict that people should be most likely to legitimize inequalities in their systems when they perceive those systems as stable and unchanging. In one study, participants who witnessed stability, rather than change, in the domain of gender equality in business subsequently reported less willingness to support programs designed to redress inequalities in completely unrelated domains. In a second study, exposure to the mere concept of stability, via a standard priming procedure, led participants to spontaneously produce legitimizing, rather than blaming, explanations for existing gender inequality in their country. This effect, however, emerged only among politically liberal participants. These findings contribute to an emerging body of research that aims to identify the conditions that promote, and those which prevent, system‐justifying tendencies. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".