The risks of inclusion: shifts in governance processes and upgrading opportunities for cocoa farmers in Ghana
Bibliographic record
Abstract
This PhD study provides a detailed description and analysis of upgrading opportunities for small-scale cocoa farmers in Ghana. It shows how and why producers do, or do not, benefit from being inserted in a global value chain that is increasingly driven by multinational cocoa processors and chocolate manufacturers. The study contributes to the recent discussions on hybrid governance structures, in which both public and private actors play a role. Ghana provides a unique case because, unlike in other West African countries, its cocoa sector is only partially liberalized. The state still plays a strong role in the cocoa market. As ‘balancer’ the state mitigates some of the risks involved in cocoa production for producers and international buyers of cocoa. However, the state is also a ‘bottleneck’, as it prevents other public, private and civil actors from playing a more active role in the supply chain. The study explores the processes of in- and exclusion of cocoa farmers in value chains and highlights two ‘risks of inclusion’. First, for producers the arrangements within the chain are sub-optimal, and do not create incentives for farmers to behave as entrepreneurs. Moreover, farmers do not benefit equally from the arrangements in place. Second, the state is inward oriented and lacks an adaptive approach to global market changes, which entails a risk for the sector as a whole.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".