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Record W4367857443 · doi:10.5539/jsd.v16n3p119

Overview of Brazilian Geographical Indications and the Experience of Cachaca Indications of Procedure

2023· article· en· W4367857443 on OpenAlexvenueno aff
Cleiton Braga Saldanha, Daliane Teixeira Silva, Luís Oscar Silva Martins, Jerisnaldo Matos Lopes, Marcelo Santana Silva

Bibliographic record

VenueJournal of Sustainable Development · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsGeographical indicationIntellectual propertyContext (archaeology)Product (mathematics)GeographyAsset (computer security)LocationService (business)StatuteRegional scienceBusinessPolitical scienceArchaeologyLawMarketingComputer science

Abstract

fetched live from OpenAlex

The Geographical Indication (IG) is an Industrial Property asset that relates and distinguishes the geographic origin of a product or service. In Brazil, it can be classified as an Indication of Origin (IP) or Denomination of Origin (DO). This study aims to provide an overview of the deposits of Geographical Indications in Brazil, from the publication of Law nº 9.279, of May 14, 1996, the Industrial Property Law, and the recognition of Indications of Origin and Denomination of Origin by the National Institute of Intellectual Property (INPI) between 1996 and 2022. Given this context, theoretically based on the concepts of territory and territoriality, the highlight of initiatives to register GIs of Cachaça in Brazil is highlighted. The exploratory research was carried out through secondary sources and the method chosen was of a qualitative nature, using the techniques of bibliographic and document review. As a result, it was found that, during the study period, there was an expansion in the number of GI records, concentrated mainly in the Southeast and South regions, but far below the existing potential in Brazil, given the existence of socioeconomic, geographic factors, environmental, ethnocultural, institutional, in addition to the characteristics of agricultural activity. Furthermore, the number of IPs registered with the INPI corresponds to approximately 70% of Brazilian GIs and only 3 of them have cachaça-type sugarcane brandy as a product.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.257
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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