Not all inequalities are created equal: Inequality framing and privilege threat for advantaged groups
Bibliographic record
Abstract
This paper investigates when and why members of privileged groups choose to describe inequality using disadvantage frames (e.g., “women have lower wages than men”) or advantage frames (e.g., “men have higher wages than women”). Four studies (N = 1,251) test the hypothesis that advantage frames are more threatening than disadvantage frames for privileged groups, and that privileged groups may strategically avoid using advantage frames when discussing illegitimate—but not legitimate—inequality. In Study 1, members of a privileged group (White Americans) exhibited more behavioral and cardiovascular indicators of threat when reading about, reflecting on, and discussing racial inequality framed as White advantage versus Black disadvantage. In Studies 2–4, members of privileged groups (but not underprivileged groups) used advantage frames less often when describing illegitimate inequality than when describing legitimate inequality. These studies suggest that subtle linguistic changes in descriptions of inequalities can threaten privileged groups, and that privileged groups may adjust their descriptions of inequality depending on its legitimacy.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".