“Fair” Inequality? Attitudes toward Pay Differentials: The United States in Comparative Perspective
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
Are American attitudes toward economic inequality different from those in other countries? One tradition in sociology suggests American “exceptionalism,” while another argues for convergence across nations in social norms, such as attitudes toward inequality. This article uses International Social Survey Program (ISSP) microdata to compare attitudes in different countries toward what individuals in specific occupations “do earn” and what they “should earn,” and to distinguish value preferences for more egalitarian outcomes from other confounding attitudes and perceptions. The authors suggest a method for summarizing individual preferences for the leveling of earnings and use kernel density estimates to describe and compare the distribution of individual preferences over time and cross-nationally. They find that subjective estimates of inequality in pay diverge substantially from actual data, and that although Americans do not, on the average, have different preferences for aggregate (in)equality, there is evidence for:
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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