<scp>F</scp>air <scp>T</scp>rade Awareness and Engagement: A Coffee Farmer's Perspective
Bibliographic record
Abstract
Abstract As one of the world's most traded commodities, coffee has been criticized for its contribution to environmental degradation, social injustice, and economic disparities between the producing regions of the Global South and consuming countries of the Global North. However, the Fair Trade concept is promising to change this through the establishment of a trading system where producers, importers, and processors form a more direct network characterized by an established set of ethical principles and practices deemed as “fair.” While the transformational benefits of Fair Trade at the producer level have been examined in several impact studies, the farmers' low awareness and understanding of Fair Trade concepts, principles, markets, and customers represent an issue of concern. Among the many benefits associated with increased Fair Trade awareness is greater participation in Fair Trade governance bodies and the long‐term viability of the market itself. This research looks at Fair Trade from the perspective of farmers and their cooperative and uses the case of Cooperativa Agraria Cafetalera Pangoa, Peru, to examine how Fair Trade awareness (defined as knowledge of different areas of Fair Trade) is understood at the producer level. We then develop a three‐level Fair Trade awareness Model that illustrates both the existing and desired levels of understanding with regard to Fair Trade awareness.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".