The civic participation in China survey: key trends in philanthropic and voluntary activities
Bibliographic record
Abstract
The Civic Participation in China Survey (CPCS) is a nationwide, randomized online study of urban residents, conducted in four waves between 2018 and 2024. It examines philanthropic and volunteering activities, as well as perceptions of citizenship and civic engagement. This article outlines the survey’s methodology and analyzes trends in civic participation in mainland China, exploring connections to democratization and good governance. The study highlights complex dynamics in volunteerism under authoritarian rule. Volunteers acquire ‘citizen skills’ to navigate social problem solving, but generally reinforce state authority rather than challenge it. By 2024, respondents increasingly believed the state could handle crises independently, reflecting rising political centralization and performance legitimacy under Xi Jinping. Distinctions between state-led and citizen-led volunteerism emerge, with skepticism toward the authenticity of state-driven efforts. While civic participation fosters social trust and governance improvements, resistant ‘bad citizens’ reveal challenges in promoting philanthropy. These findings emphasize China’s delicate balance in managing civil society.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".