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A Importância dos Estilos de Formatação de Trabalhos Acadêmicos

2023· article· pt· W4381739168 on OpenAlexaboutno aff
Camila Caroline Luz Pacheco Costa, Franchys Marizethe Nascimento Santana, Geovanna Lara de Souza Borges, Jordan Silva Rodrigues, Lidiane Paiva Dias, Paola de Almeida de Albuquerque, P. Tomé, Ricardo Almeida Garcia, Thaylane Araújo e Silva, Yanne Eufrázio de Carvalho, Rafael Lemes de Aquino

Bibliographic record

VenueBrazilian Journal of Implantology and Health Sciences · 2023
Typearticle
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Introdução: A pesquisa científica tem por objetivo gerar novos conhecimentos, de forma a contribuir com os diversos segmentos da sociedade e proporcionar melhor qualidade de vida para a população, utilizando de métodos e técnicas específicas para esta finalidade. Para que a disseminação destes conhecimentos ocorra de forma ética e eficaz, o pesquisador deve estar atento a alguns aspectos importantes, como a redação científica, a qual inclui as referências bibliográficas e a normatização dos trabalhos científicos. Objetivo: Descrever as principais características dos estilos de formatação de trabalhos científicos: ABNT, APA, Chicago, MLA e Vancouver. Material e Método: Foi realizada uma revisão bibliográfica no banco de dados Scientific Electronic Library (SCIELO), Biblioteca Virtual em Saúde Brasil (BVS) e Google Acadêmico. Revisão de Literatura: As normatizações são importantes, pois contribuem com a padronização e estruturação dos insumos do desenvolvimento tecnológico e científico, de forma eficaz e com qualidade. Considerações Finais: Espera-se que o conteúdo compilado oriente o leitor/pesquisador quanto a aplicabilidade das normas técnicas de normalização dos estilos de padronização de trabalhos científicos comumente recomendados por periódicos nacionais e internacionais com o intuito de ter um alcance e disseminar o conhecimento produzido.

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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.471
Teacher spread0.260 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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