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Record W4402815866 · doi:10.48143/rdai.27.rossi

Comitê de resolução de disputas: referências de utilização no Brasil

2023· article· pt· W4402815866 on OpenAlexaff
Alina de Toledo Rossi, Karen Cristina Moron Betti Mendes

Bibliographic record

VenueRDAI | Revista de Direito Administrativo e Infraestrutura · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicBrazilian Legal Issues
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Para cada conflito existe uma solução mais adequada. Além das formas autocompositivas já consagradas no ordenamento jurídico brasileiro, como a mediação e a conciliação, e a arbitragem como forma de solução adjudicada, outras formas podem ser utilizadas na prevenção e resolução de conflitos. O presente trabalho objetiva apresentar o Comitê de Resolução de Disputas, Comitê de Especialista ou Dispute Resolution Board como forma eficaz na prevenção e resolução de conflito, além de demonstrar que o instituto já vem sendo utilizado no Brasil com resultados positivos.

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)0.020
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.053
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.009
Science and technology studies0.0090.012
Scholarly communication0.0110.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.053
GPT teacher head0.368
Teacher spread0.316 · 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 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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