Which community network structures can support sustainability programs? The case of the Sustainable Cocoa Production Program in Indonesia
Bibliographic record
Abstract
Smallholder farming is a source of livelihoods and food for many but also a major contributor to global environmental challenges. Prominent studies have found connections between the productivity and sustainability of individual smallholders’ practices and their positions in local social networks. However, the role of entire community network structures for the adoption of diverse types of practices is relatively less understood. This matters because findings from individual-level network studies of adoption of single practices do not necessarily scale up to provide relevant implications for wide-ranging landscape-level problems. This study seeks to answer the following: (1) Which community network structures are associated with adoption of a broad range of practices recommended by a sustainability program? (2) Which community network structures are associated with farmers’ adoption of similar practices as their peers within the same community? We examine a program in Sulawesi, Indonesia that aimed to reduce greenhouse gas emissions from cocoa while sustaining productivity, using data that includes over 5000 peer-to-peer social ties of 4573 individuals in 70 villages and their adoption of 22 practices. Multiple linear regressions showed that communities with more cohesive network structures tend to display more homogeneous practices. However, the adoption of recommended practices in such communities was generally lower than in less cohesive community networks where internal social influence might be weaker and openness to experiment with diverse externally introduced practices higher. This case illustrates a situation where community bonding social capital may not support an intervention aiming at greenhouse gas reduction and it provides some suggestions why the same program may be more effective in some communities than others.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".