Volunteerism and democratic learning in an authoritarian state: the case of China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it