The political ecology of cocoa in <scp>Ghana</scp>: Past, present and future challenges
Bibliographic record
Abstract
Abstract In recent years, many have raised concerns regarding the need to develop sustainable practices in the cocoa sector and to prepare for impending threats associated with climate change. Although evidence suggests more needs to be done to address the environmental concerns surrounding cocoa, the sustainability of the sector cannot be understood without recognizing other socio‐economic and socio‐political factors. This paper uses a case study of Ghana—the world's second largest producer of cocoa—to problematize the often‐simplistic claims concerning the fate of the crop. While the paper draws upon a diverse disciplinary body of literature, it uses a political ecology lens to analyse the multifaceted problems facing the cocoa industry. The paper derives insights from 131 interviews conducted with a wide range of stakeholder groups in Ghana's capital (Accra) and leading cocoa‐producing regions (Ashanti and Western). The analysis reveals the need to challenge dominant narratives about the cocoa‐climate change nexus, as it highlights other threats to the sector. This includes the difficulty of securing youth labourers, the problem of “galamsey” (i.e., illegal artisanal mining), the issue of land scarcity, and the politicization of migrant workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".