Cocoa Production in Ghana (1879-1976)
Bibliographic record
Abstract
Abstract Cocoa production has been a major source of income and revenue to many citizens and the governor of Ghana respectively through time. Historically, although attributed to Tetteh Quarshie, records have shown that prior to Tetteh Quarshie’s achievement, the Dutch and Basel Missionaries had experimented with the crop in the Gold Coast. Since its introduction in the country, cocoa production has expanded and spread across all the regions in Ghana. The production of cocoa has affected every facet of development in the country since its inception and has once led Ghana to be world’s major exporter of the beans. Cocoa production in Ghana has gone beyond its agricultural and economic significance with its impacts felt across socio-cultural, religious and political life of Ghanaians. That notwithstanding, scholars have made partial effort at addressing the impact of cocoa production among Ghanaians between 1879 and 1976. Using a qualitative approach rooted in both primary and secondary sources, the current study sought to address the gap aforementioned by tracing the relationship between cocoa production and economics, politics and social-religious practices among Ghanaian between 1879 and 1976. Findings from the discourse revealed that though an agricultural product, cocoa can no longer be said to belong to that sphere alone. The product and its associated gains have permeated the entire life of Ghanaians since its inception.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".