Economic Legitimation in a New Era: Public Attitudes to State Ownership and Market Regulation in China
Bibliographic record
Abstract
Abstract Autocrats typically seek public support on the basis of economic growth-promotion and redistribution policies, and China is no exception. As important as these factors are for authoritarian resilience, we argue that economic legitimation is a more complex phenomenon than has previously been acknowledged. Beyond improvements in material well-being, citizens form judgements about the state's effectiveness in carrying out a variety of economic roles beyond growth promotion and they also care about the fairness of these market interventions. In this study, we use original survey data collected in late 2015 and early 2016 to evaluate Chinese citizens’ perceptions of two economic roles of the state that have been hotly debated in recent years: state ownership and market regulation. We find that while citizens view the ideas of state ownership and interventionist regulation in a generally positive light, suggesting a broad level of agreement in Chinese society about what economic functions the state ought to perform, perceptions of how the state actually carries out these roles are more mixed. Our results show that the urban young are especially inclined to critical evaluations, raising the question of how the Chinese Communist Party's legitimation strategy will fare under conditions of inter-generational value change.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".