Seeing the trees: Farmer perceptions of indigenous forest trees within the cultivated cocoa landscape
Bibliographic record
Abstract
Throughout Ghana’s high forest zone, cocoa farmers clear secondary or primary forest to establish new farms, capturing the capacity of nutrient-rich forest soils to increase cocoa yields. However, many cocoa farmers preserve remnant forest trees on existing farms as an integral and necessary component of the production landscape, making decisions about tree removal and tree retention based on a unique set of selection criteria. How they perceive trees plays a crucial role in daily management decisions made at the micro level, which in turn influence landscape patterns on the macro level. The central question of this research relates to how Ghanaian farmers perceive forest trees within the cultivated cocoa landscape. The research data were collected using an exploratory case study approach that combined ethnographic and survey techniques, and draws on 34 farmer interviews, 34 farm surveys, and interviews with key informants representing diverse stakeholder interests in the Domeabra Traditional Lands, Ashanti-Akim in south central Ghana. The research data were analyzed to identify the important functions of forest trees as perceived by study participants, both as a biophysical component within the farm ecosystem and as an input to the rural economy.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".