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AS CONSEQUÊNCIAS DOS IMPOSTOS NO COMÉRCIO JUSTO EM COOPERATIVAS RURAIS BRASILEIRAS: O CASO DOS CAJUCULTORES DO ESTADO DO PIAUÍ

2023· article· pt· W4384131747 on OpenAlexaff
Francisco Francirlar Nunes Bezerra, Manoel de Jesus Nunes da Costa, Márcia Gabrielli Sousa Campêlo Marinho, Ricardo Henrique Chaves Martins

Bibliographic record

VenueRevista Foco · 2023
Typearticle
Languagept
FieldEnvironmental Science
TopicRural Development and Agriculture
Canadian institutionsDiscovery Air (Canada)Privy Council Office
Fundersnot available
KeywordsPolitical scienceHumanitiesBusinessArt

Abstract

fetched live from OpenAlex

É consensual a essencialidade do cooperativismo como elemento que contribui para o desenvolvimento socioeconômico, notadamente pela característica de distribuição de resultados entre seus participantes. Além do cooperativismo, os cajucultores, em particular do Piauí, ao buscarem ampliar suas vendas no intuito de aumentar suas receitas e a geração de empregos, encontraram no Comércio Justo uma alternativa viável. Nesse contexto, o sistema tributário tem dificultado tal objetivo, haja vista torna-se um custo bem oneroso. Assim, objetiva analisar a aplicação dos impostos brasileiros e suas consequências sobre o Comércio Justo nas cooperativas rurais, especificamente, da Central de Cooperativas do Estado do Piauí – COCAJUPI. Para tanto, fez-se uma pesquisa bibliográfica, documental e de campo, em que se verificou que a definição de ato cooperativo, tem sido restrito e limitando os ganhos com o Fisco. Ademais, o Sistema Tributário Brasileiro tem implementado normas e cobranças as cooperativas, a exemplo da COCAJUPI, sem distinções ou privilégios, a exemplo do imposto de exportação de castanha sem casca que tem 30% de cobrança sobre o valor do produto. Concluiu-se que apesar do apoio do Estado do Piauí as cooperativas como a criação da Política de Apoio ao Cooperativismo em 2016, ainda carece de ações no segmento da cobrança de tributos.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.060

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 head0.014
GPT teacher head0.258
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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