Constructed Hierarchical Government Trust In China: Formation Mechanism And Political Effects
Bibliographic record
Abstract
The Chinese government has long enjoyed a higher level of popular trust in its central authority than in its local governments, which means that the Chinese public’s trust in government is hierarchical. While existing research has highlighted hierarchical trust’s role in bolstering the Chinese regime’s rule, the formation mechanism for such trust has not been adequately explored empirically. In this paper, we use data from the China General Social Survey (2010) to explore the formation mechanism of hierarchical government trust and find that economic development, adherence to traditional values, and high frequency of Internet usage all contribute to the decrease of hierarchical government trust. These findings challenge conventional views that cultural traditions and Internet use help sustain hierarchical government trust and show that propaganda is the only variable that sustains the pattern of hierarchical government trust. We further challenge existing literature that views hierarchical government trust as a natural outcome of China’s hierarchical administrative structure and empirically prove that such trust is in fact intentionally constructed by the central government through propaganda campaigns and an institutional design aimed at strengthening the central government’s authority and at
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".