Inequality in researchers’ minds: Four guiding questions for studying subjective perceptions of economic inequality
Bibliographic record
Abstract
Abstract Subjective perceptions of inequality can substantially influence policy attitudes, public health metrics, and societal well‐being, but the lack of consensus in the scientific community on how to best operationalize and measure these perceptions may impede progress on the topic. Here, we provide a theoretical framework for the study of subjective perceptions of inequality, which brings critical differences to light. This framework—which we conceptualize as a series of four guiding questions for studying subjective perceptions of economic inequality —serves as a blueprint for the theoretical and empirical decisions researchers need to address in the study of when , how , and why subjective perceptions of inequality are consequential for individuals, groups, and societies. To lay the foundation for a comprehensive approach to the topic, we offer four theoretical and empirical decisions in studying subjective perceptions of inequality, urging researchers to specify: (1) What kind of inequality? (2) What level of analysis? (3) What part of the distribution? and (4) What comparison group? We subsequently discuss how this framework can be used to organize existing research and highlight its utility in guiding future research across the social sciences in both the theory and measurement of subjective perceptions of inequality.
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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 | 0.076 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".