Inequality in People’s Minds: An Integrative Psychological Framework of Perceptions of Economic Inequality
Bibliographic record
Abstract
Academic Abstract People’s perceptions of economic inequality are important predictors of their political attitudes and behaviors. Scholars across the social sciences have worked to understand people’s (mis)perceptions of inequality. Yet, scholars currently lack a common framework for integrating emerging findings and conceptualizing how these perceptions are formed. Here, we propose an integrative framework to help researchers highlight the psychological processes underlying how inequality is perceived. We draw on theories of perception, cognition, developmental, and social psychology to identify five interlinked, iterative components of the inequality perception process: (a) access to inequality cues, (b) attention to these cues, (c) comprehension of these cues, (d) motivated processing of these cues, and (e) meaningful summary representation of inequality. Our framework provides a roadmap for integrating research across disparate fields, making sense of current findings, and identifying novel challenges to advance future research. Public Abstract How much inequality people perceive better predicts their political action than do official measures of inequality (e.g., economic indicators like the Gini coefficient). While scholars across the social sciences are working to understand these (mis)perceptions of inequality, the literature lacks agreement on measurements of inequality perceptions and, as a result, on whether people under or overestimate inequality. By providing an integrative psychological framework for inequality perceptions that focuses on the processes underlying how people form these perceptions and what they mean to them we shed light on when and why people perceive more or less inequality. Our framework outlines the psychological processes underlying perceptions of inequality and helps scholars value the information and insight people’s own perceptions provide for addressing inequality in communities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".