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Record W2797065973 · doi:10.22481/recuesb.v4i8.3587

PET NAS ESCOLAS: O CONHECIMENTO “PLURIVERSITÁRIO” PROMOVIDO PELO PET ECONOMIA DA UNIVERSIDADE ESTADUAL DO SUDOESTE DA BAHIA

2018· article· pt· W2797065973 on OpenAlexaff
Olga Hianni Portugal Vieira, Faíque Ribeiro Lima

Bibliographic record

VenueRevista Extensão & Cidadania/Revista Extensão e Cidadania · 2018
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsHumanitiesPhilosophyPsychology

Abstract

fetched live from OpenAlex

Este trabalho objetiva apresentar a relação extramuros desenvolvida pelo PET Economia daUESB com o trabalho intitulado “PET nas Escolas” como uma forma de auxiliar o aluno pré-vestibulando com a escolha do curso de graduação. Com isso, espera-se contribuir para aredução dos problemas sociais, acadêmicos e econômicos relacionados à retenção/evasão dediscentes no curso de Economia na UESB. O projeto consistiu em apresentar para estudantes doensino médio de uma rede estadual de educação em Vitória da Conquista, o Centro Territorialde Educação Profissional- CETEP, as características do curso de Ciências Econômicas, de modogeral, e a particularidades do curso na UESB. Foram retratados na sala de aula o objeto docurso, o tempo de duração, o fluxograma e as áreas no mercado de trabalho do economista.Adotou-se como material a utilização de slides para apresentação das informações e o métodopara realizar tal apresentação foi a exposição oral por parte dos (das) bolsistas do Programa. Emseguida, houve espaço para a fala dos estudantes do CETEP e um bate papo com os expositorese o tutor do Programa. Ao finalizar o projeto, os (as) alunos (as) da instituição se mostraramsatisfeitos (as) pelo conhecimento adquirido e aqueles (as) que tinham o curso de Economiadentro do seu leque de opções, relataram mais segurança para a escolha do curso.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.316
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.004
Science and technology studies0.0070.005
Scholarly communication0.0100.006
Open science0.0050.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0280.011

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 head0.045
GPT teacher head0.340
Teacher spread0.294 · 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
Published2018
Admission routes1
Has abstractyes

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