Diseño metodológico de una guía de acceso al comercio justo a través del análisis del caso UROCAL
Bibliographic record
Abstract
Fair Trade was created as an initiative of international cooperation with countries of \nthe North to small farmers in the south, with the aim of helping their communities to \nreduce poverty and exploitation through the creation of supportive relationships. But \nthe global problem, as well as in Ecuador is that small producers are not part of a fair \nchain. In addition, they don´t know other commercial alternative. For this reason this \nresearch, aims to generate a methodological guide and knowledge for small \nproducers to access into Fair Trade, prior to the presentation and analysis of a \nparticular case of our country, which is located in the coastal zone of the provinces of \nOro, Azuay and Guayas, known as UROCAL and the base group NUEVO \nMUNDO, who exports organic and Fair Trade bananas to markets like Germany, the \nU.S. and Canada.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".