China’s ‘bad citizens’: understanding non-participation in philanthropic and voluntaristic activities
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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