Stability and the justification of social inequality
Why this work is in the frame
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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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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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