Beyond ecological synergies: examining the impact of participatory agroecology on social capital in smallholder farming communities
Bibliographic record
Abstract
The pivotal role of social capital in smallholder agriculture is widely acknowledged. The growth effect of social capital manifests in how networks and trust facilitate access to productive resources and knowledge sharing among farmers. While sub-Saharan Africa is considered a storehouse of rich social capital, recent literature indicates its rapid depletion due mainly to the rise of capitalist agriculture and concomitant reorganization of the relations of production that characterize smallholder agriculture. Agroecology is an alternative approach to agriculture aimed at addressing the adverse impacts of capitalist agriculture, including improving farmer-to-farmer networks. In this paper, we draw on longitudinal data from a five-year participatory agroecology intervention in Malawi using Difference-in-Difference (DID) to compare the social capital endowment of agroecology-practicing households (n = 514) and a control group of non-agroecology households (n = 400). We further employed linear regression to examine the relationship between social capital and agroecology adoption. Results from the DID analysis show a positive and statistically significant change in mean social capital for participatory agroecology households (β = 0.325, p< 0.001) compared to non-agroecology households (β = 0.108) after accounting for theoretically relevant factors. Overall, the average treatment effect of the intervention on social capital was positive (β = 0.217, p< 0.01). We also found a bidirectional relationship between social capital and adoption of agroecology practices (β = 0.12, p< 0.001). These findings reveal the positive inroads of agroecology beyond the farm-level and demonstrate the potential for policymakers to leverage these benefits to promote sustainable agriculture.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".