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Record W1949139500 · doi:10.22456/1982-8918.52925

POR UMA SOCIOLOGIA PÚBLICA DO ESPORTE NAS AMÉRICAS: UM CHAMADO EDITORIAL EM PROL DE UMA EDUCAÇÃO FÍSICA SOCIALMENTE RELEVANTE

2015· article· pt· W1949139500 on OpenAlexaff
Peter Donnelly, Alex Branco Fraga, Ángela Aisenstein

Bibliographic record

VenueMovimento (Porto Alegre) · 2015
Typearticle
Languagept
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPolitical scienceManifestoPhilosophyLaw

Abstract

fetched live from OpenAlex

Trata-se de um ensaio produzido de forma conjunta pelos três editores do número especial da revista Movimento – Por uma sociologia pública do esporte nas Américas: conquistas, desafios e agendas emergentes, proposto em comemoração ao vigésimo aniversário desta revista brasileira e inspirado nos dez anos do manifesto de Burawoy em favor de uma sociologia pública. O texto está divido em três seções, a primeira, escrita originalmente em inglês, discute a importância de se praticar uma sociologia do esporte de caráter público e de se produzir pesquisas que efetivamente contribuam para o enfrentamento político de problemas concretos da sociedade. A segunda, escrita originalmente em português, trata dos efeitos do "produtivismo" acadêmico na política de comunicação científica brasileira, em especial para as ciências humanas e sociais, destacando a especificidade da Movimento na veiculação da produção sociocultural e pedagógica da educação física. A terceira, escrita originalmente em espanhol, cita os desafios enfrentados pela equipe editorial para a organização do número especial e apresenta de forma panorâmica o conteúdo dos textos que compuseram um chamado à produção científica socialmente relevante no campo.

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)0.006
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.005
Scholarly communication0.0090.004
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.002

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 head0.055
GPT teacher head0.360
Teacher spread0.304 · 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
GenreEditorial

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

Citations3
Published2015
Admission routes1
Has abstractyes

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