Political Polarization and the Dynamics between Actual Income and Perceived Income Inequality in the United States, 1987 to 2021
Bibliographic record
Abstract
The rich often perceive lower levels of inequality than the poor. In recent decades, however, notions regarding the equality or inequality of our society have progressively taken on a more political nature. Consequently, people’s perceptions of income inequality may be less associated with their actual income status and more with their political ideology. The authors visualize this “political turn” using data from the U.S. General Social Survey (1987–2021). The analysis shows that historically actual income and perceived inequality had an inverse relationship, independent of political alignment. Yet since 2000, this has changed: whereas Republicans show a deepening inverse correlation after some attenuation in prior years, Democrats reverse it. With this said, we see an increase in overall concern about inequality among those who identify strongly with either Democratic or Republican ideologies, but importantly the biggest increase is among those in the Democratic group. This invites reflections on the nature of the “political turn.”
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".