Volunteerism and democratic learning in an authoritarian state: the case of China
Bibliographic record
Abstract
Extant literature on civic participation in Western democracies demonstrates a linear relationship between increased civic participation and a stronger democracy. In general, the scholarly debate revolves around the precise causal mechanisms for this relationship: holding government accountable; citizens learning “democratic skills”, such as collective mobilization and advocacy; and, building social capital and trust to overcome the dilemma of collective action. Given rapidly increasing volunteerism in China, this study tests these theories in a single-party authoritarian system using evidence from the 2020 Civic Participation in China Survey. The study finds that volunteers in China do learn “citizen skills”; however, these differ from those learned by volunteers in democracies. Foremost, while volunteering allows for authoritarian citizens to learn and differentiate channels most appropriate for addressing specific social problems, they generally do not try to directly hold their government accountable for poor performance. Additionally, the study finds limited support that volunteers are seeking to develop trust in other citizens, contra evidence from Western democracies. Finally, the results suggest that volunteers are participating as a means to send signals to the state that they are emerging local community leaders. These findings have important implications for increasing civic participation in authoritarian regimes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".