MétaCan
Menu
Back to cohort
Record W4400187946 · doi:10.9771/ell.v0i77.55220

IMAGEM, REPRESENTAÇÃO E EMOÇÃO NOS TOTENS DE TORCEDORES DE FUTEBOL NAS ARQUIBANCADAS DURANTE A PANDEMIA

2024· article· pt· W4400187946 on OpenAlexaff
Anna Gabriela Rodrigues Cardoso, Nara Bretas Lage, Leandro Henrique Ferreira Cardoso

Bibliographic record

VenueEstudos Linguísticos e Literários · 2024
Typearticle
Languagept
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsThinkpath Engineering Services (Canada)
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

O artigo analisa a representação do torcedor de futebol nas arquibancadas por totens de papelão em jogos disputados sem a presença física da torcida durante as restrições da COVID 19. Objetiva-se identificar: como a imagem pode comunicar e transmitir mensagens a partir dos estudos de Joly (2007) e os efeitos patêmicos gerados pelas imagens do torcedor, a partir de Charaudeau (2015); compreender as relações torcedor-arquibancadas antes e durante a pandemia, segundo Teixeira (2013); comparar a realidade da torcida de futebol no período da crise sanitária. Seleciona-se imagens a partir da relevância para o nosso foco temático, considerando o período de proibição e de retorno da presença de torcidas nas arquibancadas. Coletou-se duas fotografias de torcedores na arquibancada em jornais esportivos e duas em plataformas de fotos. Encontra-se nas fotografias torcedores representados por totens e ainda há a fotografia em que se misturam pessoas e totens, além da que retrata a torcida em tempos “normais”. Estas quatro imagens constroem, propositalmente elencadas por nós, uma narrativa que é analisada neste trabalho. Conclui-se que, a partir dos totens, reconstroem-se imaginários de torcidas com representações que podem ser retomadas na memória sobre ser torcedor na pandemia.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.366
Teacher spread0.333 · 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 designQualitative
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

Explore more

Same venueEstudos Linguísticos e LiteráriosSame topicPhysical Education and Sports StudiesFrench-language works237,207