The News Media and the Politics of Inequality in Advanced Democracies
Bibliographic record
Abstract
What has allowed inequalities in material resources to mount in advanced democracies? This chapter considers the role of media reporting on the economy in weakening accountability mechanisms that might otherwise have incentivized governments to pursue more equal outcomes. Building on prior work on the United States, we investigate how journalistic depictions of the economy relate to real distributional developments across OECD countries. Using sentiment analysis of economic news content, we demonstrate that the evaluative content of the economic news strongly and disproportionately tracks the fortunes of the very rich and that good (bad) economic news is more common in periods of rising (falling) income shares at the top. We then propose and test an explanation in which pro-rich biases in news tone arise from a journalistic focus on the performance of the economy in the aggregate, while aggregate growth is itself positively correlated with relative gains for the rich. The chapter’s findings suggest that the democratic politics of inequality may be shaped in important ways by the skewed nature of the informational environment within which citizens form economic evaluations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".