How Does Actual Inequality Shape People’s Perceptions of Inequality? A Class Perspective
Bibliographic record
Abstract
While some scholars suggest that awareness of income inequality is strongest when the actual level of inequality is high, others find that individuals’ awareness of income inequality is largely unresponsive to actual inequality. In this article, we argue that individuals in different social class positions often respond to the actual levels of income inequality distinctively, and therefore a class perspective is essential in understanding how actual inequality and people’s perceptions of it are associated. Using data from the social inequality modules of the International Social Survey Programme (ISSP, 1992, 1999, and 2009) as well as the World Income Inequality Database ( https://www.wider.unu.edu/ ) and the World Inequality Database ( https://wid.world/ ), we consider how actual inequality interacts with social class to shape people’s perceptions of income inequality across 64 country-years between 1992 and 2009. We find that overall, perceptions of inequality are higher among the working class and lower among salariats. However, cross-nationally and over time, as the actual level of inequality increases, working classes become less critical toward inequality, whereas salariats become more critical. The actual level of inequality itself has no impact on people’s discontent toward it. This creates a counterbalancing effect that obscures the aggregate relationship between rising inequality and people’s perceptions of it.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".