From Direct Involvement to Indirect Control? A Multilevel Analysis of Factors Influencing Chinese Foundations’ Capacity for Resource Mobilization
Bibliographic record
Abstract
Abstract Some nonprofits easily attract resources, while others struggle to survive. However, little is known about what characteristics account for the difference in nonprofit organizations’ capacity to mobilize resources, especially in authoritarian countries. Using multilevel modeling techniques and a national sample of 3344 philanthropic foundations in 31 regions of mainland China, this research seeks to address this knowledge gap by examining the effect of both organizational and contextual factors on foundations’ revenues, paying special attention to the government’s role. Results show that the distribution of resources is highly unbalanced in China’s foundation sector and that foundations with particular characteristics are systematically favored. By exploring what factors give foundations the edge in mobilizing resources, this study reveals how the Chinese government has used a more sophisticated, indirect method than direct control to shape resource distribution and regulate the development of nonprofits. Social organizations can survive and even thrive, but only certain types.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".