China’s ‘bad citizens’: understanding non-participation in philanthropic and voluntaristic activities
Bibliographic record
Abstract
In response to increasing socio-economic inequalities, the Chinese state has promoted the idea of the ‘good citizen’ who engages in philanthropy and volunteerism. This study explores why some individuals in China choose the converse, to be ‘bad citizens’ by not participating in these activities. Utilizing data from four waves of the Civic Participation in China Surveys (CPCS) conducted in 2018, 2020, 2022 and 2024, the study suggests that the behaviour of such non-participants are influenced by their immediate social circle, their general perceptions of donating and volunteering, and their level of support for the government. These findings have significant implications. The existence of bad citizens conceptually highlights the presence of a ‘skeptical citizen’ who does not fully align with the state’s vision of the model citizen. At a more general level, the study provides a profile of bad citizens that enables the development of targeted policies to incentivize charitable giving and volunteering, and promote greater civic engagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".