Policy Polarization, Income Inequality and Turnout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Past research on the relationship between income inequality and turnout has produced mixed results, with some studies suggesting that income inequality leads to lower turnout while other studies find little or no significant effects. In this article, we investigate the extent to which these mixed results are due to the contingent nature of inequality on turnout, which depends upon the nature of the policy options that are presented to the electorate. We test these expectations on data from national elections in 30 established democracies from 1965 through 2017 covering 300 elections. Regression analysis using country-level fixed effects reveals consistent evidence in favor of our hypotheses: Inequality tends to have a negative impact on turnout, especially in depolarized party systems, but as party system polarization increases the negative impact of inequality is mitigated.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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