Coffee Production and Geographical Indications (GI): An Analysis of the World Panorama and the Brazilian Reality
Bibliographic record
Abstract
Coffee cultivation is of great importance in the world economy. Due to consumers' demand for products with quality and geographic certification, the topic is relevant. The research objective is to portray the international and Brazilian scenario of the coffee production chain, based on production and Geographical Indications (GIs) for the product. The research is classified as exploratory and descriptive in relation to the approach, and as bibliographical and documental in relation to the means of investigation. It was found that the world's largest coffee producers are Brazil, Vietnam, Colombia, and Indonesia. There was a reduction in world production for the 2021/22 crop, due to the low production of arabica coffee in Brazil, but for the 2022/23 crop, an increase in this production is estimated. Most coffee-producing countries follow specific legislation to protect Geographical Indications and others protect them through trademarks. In Brazil, the definition of GI is explained by its species, Indication of Origin (IO), and Denomination of Origin (DO). Brazil is the second with the highest number of GIs for coffee in the world. El Salvador has a GI that represents the entire coffee value chain. Indonesia is the country with the highest number of GIs for coffee in the world and has state support for its promotion. Given this scenario, there is a need to develop public policies aimed at this product. It is indicated for future research the study of these policies and the performance of bodies responsible for the consolidation of GIs in their respective countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".