The Protections Afforded by Geographical Indicators and Their Characteristics: The Case of the Gis in Brazilian Agribusiness
Bibliographic record
Abstract
Geographical Indication (GI) is a way of differentiating a product in the market by highlighting its added value and guaranteed origin. The objective in this paper was to analyze the Brazilian agribusiness GIs in order to identify the extent of the protection in this form of certification. A bibliographic survey was carried out in the public domain as well as a documentary search in the National Institute of Industrial Property (INPI) database. The database of the registries of GIs, comparing the characteristics of the GIs as: Associations and Cooperatives, in natura and modified, direct and manufactured production, animal and vegetable product, and product or service. The data of GIs was collected and grouped. Soon after, a search was conducted on the official sites of the cooperatives and associations in order to gather information about the production and other details. Direct production is very labor intensive and can not be produced in large quantities. The associations and cooperatives that have the IG seal adopt a manufactured production and hope thus have a greater financial return. All of the Brazilian agribusiness GIs are designated for products. This study serves as a basis for future research on GIs that wish to know the characteristics of Brazilian GIs in order to interact on the subject.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".