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PERCEPÇÃO DOS PERITOS CONTADORES DO ESTADO DE MATO GROSSO EM RELAÇÃO AS MUDANÇAS OCORRIDAS NO NOVO CÓDIGO DE PROCESSO CIVIL

2023· article· pt· W4387782155 on OpenAlexaff
Aline Kawakami, José Ricarte de Lima, Vanusa Batista Pereira, Enézio Mariano da Costa, Almir Rodrigues Durigon, Juliana Vitória Vieira Mattiello da Silva

Bibliographic record

VenueRevista Foco · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicBrazilian Legal Issues
Canadian institutionsGoogle (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

O Novo Código de Processo Civil (NCPC) brasileiro que entrou em vigor em 2016, trouxe várias alterações, entre as quais, mudanças relacionadas à atuação dos peritos. Nesse sentido, o presente estudo buscou verificar a percepção dos peritos contadores em relação as mudanças ocorridas no NCPC. Para tanto, elaborou-se um questionário com perguntas abertas e fechadas e encaminhado aos respondentes por meio de e-mails pessoais, obtidos no site do Cadastro Nacional dos Peritos Contadores (CNPC), utilizando-se da plataforma Google Docs. O estudo desenvolveu-se com profissionais peritos que atuam no Estado de Mato Grosso. As evidências apontadas pelo o estudo são de que 65% dos respondentes são do sexo masculino e que a faixa etária da maioria dos peritos respondentes está entre 41 a 60 anos. Os resultados revelaram ainda que, 80% dos respondentes são mais solicitados para atuarem em recuperação judicial. Em relação a percepção dos peritos contadores sobre as mudanças ocorridas no NCPC, os resultados mostraram que segundo os respondentes o NCPC ampliou o papel do perito contador, aumentando a responsabilidade do perito, mas ressaltando a cooperação que deve existir entre as partes visando solucionar os litígios de forma consensual.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.338
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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