Do climate disasters make farmers more willing to cooperate? Evidence from rural communities in southern China
Bibliographic record
Abstract
Purpose This article aims to explore the impact of climate disasters on small-scale farmers’ willingness to cooperate and explore the mediating effect of social capital. Design/methodology/approach The study investigates farmers’ willingness to cooperate through a framed field approach and surveys the information of individuals and villages, including climate disasters and social capital, using a structured questionnaire from rural communities in Jiangxi and Sichuan, China. Findings The results show that climate disasters and social capital are significant and positive determinants of farmers’ willingness to cooperate. In specific types of climate disasters, drought is positively associated with farmers’ cooperation willingness. Moreover, the mediation effect of drought on farmers’ willingness to cooperate through social capital has been demonstrated to be significant although negative, whereas the mediation effect of flood on farmers’ willingness to cooperate through social capital is significant and positive. Originality/value First, given the limited studies focusing on the impact of climate disasters on small-scale farmers’ willingness to cooperate, the authors complement the existing literature through a framed field experiment approach by designing a scenario that every farmer may encounter in their production activities. Second, the study figures out the roles of drought and flood as different kinds of climate disasters in farmers’ decision-making of cooperation and sheds light on the positive impact of climate disasters on small-scale farmers. Finally, this paper provides empirical evidence of social capital as a potential channel through which climate disasters could possibly affect farmers’ willingness to cooperate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".