Assessing the role of social networks in agricultural cooperatives in the Niayes region of Senegal
Bibliographic record
Abstract
Agricultural cooperatives are fast becoming one of the most prominent contributors to rural development internationally.Policymakers, academics, and donors have identified these cooperatives as being an essential mechanism to facilitate information exchange, improve collaboration, disseminate agricultural innovation, and improve market access among smallholder farmers in diverse settings.However, despite significant international support, empirical research on the benefits of agricultural cooperatives has been equivocal, revealing both successes and failures, and raising questions about the ability of cooperatives to equitably and sustainably facilitate change.Further, many existing studies have tended to overlook the dual social and economic identity of agricultural cooperatives, instead focusing on their economic functioning with comparatively little attention being paid to social relationships.This thesis seeks to better understand how the internal social structure of agricultural cooperatives can influence their function and performance with a view to inform research and policy.More specifically, the research seeks to 1) analyze how social networks within a formal cooperative can influence their ability to facilitate knowledge flow and innovation dissemination; 2) assess how agricultural cooperatives can contribute to developing the sustainable livelihoods of their members; and 3) inform future research and development efforts directed towards ensuring more equitable and resilience-focused agricultural cooperative policy frameworks.Using case studies in the Niayes Region of Senegal, this study reveals the complexity of the social relationships that can underpin agricultural cooperative development in Senegal and how these relationships can impact their overall performance and service provision to members.Results highlight that economic analyses of cooperatives can only partially account for the impacts of existing power arrangements, social structures, and socio-economic diversity present among smallholder farmers in developing areas, often leading to inappropriate power asymmetries and inequitable distribution of benefits.Based on our findings, agricultural development initiatives seeking to establish or collaborate with agricultural cooperatives could benefit from conducting a priori assessments of the existing social relations and networks affecting producer interactions in the cooperative.There remains a need for better merging economic analyses (i.e. the impact of cooperative membership on farm income, market access, ii! !commodity prices, etc.) with social analyses (such as who is benefiting from being a cooperative member, how are members interacting and sharing knowledge, and how are collective decisions being made) in cooperatives-related research in order to better address their dual-identity and multiple objectives in developing areas context.Such an approach has the potential to better contextualize their design, operation, and function in order to facilitate innovation and resource access for members.iii! !
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".