Collective action improves elite-driven governance in rural development within China
Bibliographic record
Abstract
Abstract Rural areas are at the forefront of achieving sustainable development goals, and elite actors tend to be the most influential local decision-makers in rural development. Nevertheless, improving the effectiveness of governance by elites and avoiding or redressing “elite capture” remain key challenges for sustainable rural development globally. This research integrates a large-scale quantitative dataset consisting of 604 villages in seven counties of Jiangsu province in China with qualitative data from eight villages in three out of the seven counties to examine whether and how collective action mediates the correlation between rural elites and rural development. Our quantitative analysis using multiple regression and path analysis indicates that collective action is a mediator, but it is more influential in linking governing elites than in linking economic elites with rural development. Our case studies with interviews further illuminate that collective action fuels rural development by improving resource reallocation and resource-use efficiency with the participation of both elites and non-elites. Innovative collective action designs that leverage a reputation effect to foster reciprocity norms promote the participation of elites while discouraging elite capture. Additionally, this research contributes to longstanding debates in commons governance about the role of authority interventions: we find evidence justifying the benefits of authority in catalyzing and sustaining collective action while also corroborating the critical role of democratization in improving rural governance by elites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| 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".