Bibliographic record
Abstract
Thomas F. Remington. The Politics of Inequality in Russia. New York: Cambridge University Press, 2011. xiv, 220 pp. Index. $29.99, paper.While a dramatic increase in income inequality in Russia since the end of communism is readily apparent, its causes are not. For one, income inequality is by definition an aggregate-level phenomenon, and it matters how the data are aggregated. The level of aggregation not only determines which incomes are included in the measure (and thus its range), but it also defines the terrain to be explored when searching for explanations. By focusing on Russia's regions, The Politics of Inequality in Russia by Thomas F. Remington considers income inequality at a meaningful, yet less commonly observed level of analysis. Disaggregating the Russian Federation into its 83 constituent subjects highlights how the regions in which Russians live directly influence their quality of life. And, as one might expect, regional differences in natural resources and differential inheritances in industrial development take one a long way toward understanding why some regions have better economic trajectories than others (p. 23). Yet, comparing Russia's regions also reveals a surprising relationship between income inequality and politics.Like most scholars, Remington began his initial inquiry with conventional wisdom in mind. Specifically, he expected polities with more democratic political institutions to experience less inequality. The question to have been answered, then, was whether the lower levels of inequality resulted from the structure of earnings themselves or from redistribution after a market-based differentiation in earnings (p. xi). The surprise, however, is that regional levels of democracy in Russia are actually correlated with higher levels of inequality, ceteris paribus. To understand this puzzle, Remington turns his sights on the relationship between economics and politics in the regions.According to Remington, regional differences in income inequality in Russia reflect the nature of co-operation between local enterprises and regional governments. In more open, pluralistic regions, business leaders were granted greater access to the policy process through consultative structures while relatively open media systems and participation in regional legislatures granted them additional checks on government. In these contexts, regional governments and enterprise managers worked together on policy related to employment, minimum wage levels and taxation, as well as competition, investment, and infrastructure development. Where regional governments could more credibly assure business leaders that they would be protected from arbitrary governmental interference, economic actors were more willing to accept their firm's social responsibilities, like tax compliance. …
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".