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Record W4390490288 · doi:10.34117/bjdv10n1-001

Perfil das propostas para o esporte e lazer nas candidaturas aos governos dos estados da Região Norte do Brasil nas eleições de 2022

2024· article· pt· W4390490288 on OpenAlexaff
Felipe Canan, Angélica Maria Pinto Fontes, Emynna Cavalcante Guimarães, Jandre Santiago Amorim De Araujo, Yan Carlos Souza da Silva, Tiago Pereira Cirino

Bibliographic record

VenueBrazilian Journal of Development · 2024
Typearticle
Languagept
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Objetivou-se analisar as propostas para o esporte e/ou lazer nas candidaturas aos governos dos estados da Região Norte nas eleições de 2022. Utilizou-se pesquisa descritiva, quantitativa e documental, buscando, a partir dos descritores “esport” e “lazer”, as propostas para o esporte e lazer nas candidaturas disponibilizadas pelo site “divulgacand”. As análises foram feitas por meio de estatística descritiva. A maioria dos candidatos é do gênero masculino e os partidos políticos com mais propostas para esporte e lazer são Partido Liberal (PL) e Movimento Democrático Brasileiro (MDB). Identificou-se pequena predominância de propostas para o esporte em relação ao lazer, apesar de forte associação entre ambos. Infraestrutura prevalece para o esporte e serviços para o lazer. De modo geral, as propostas apresentam um perfil generalista, com certa ênfase na relação à educação e cultura e atendimento ao público infanto-juvenil.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.341
Teacher spread0.314 · 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 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
Published2024
Admission routes1
Has abstractyes

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