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Índices linguísticos e para-linguísticos da gestão da emoção e da projeção de ethos no discurso de Marina Silva, no programa Jô 11/2

2014· article· pt· W2246339468 on OpenAlexaff
Cláudio Humberto Lessa

Bibliographic record

VenueCaletroscópio · 2014
Typearticle
Languagept
FieldArts and Humanities
TopicLinguistics and Education Research
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Estudos têm mostrado como a mídia transformou profundamente a eloquência política: cf. Rubim (2004) e Courtine (2006). Passou-se a adotar uma retórica mar­cada por um estilo dialogado e familiar; valorizam-se mais as imagens e a vida pri­vada dos políticos que suas ideias. Neste artigo, apresento o resultado de uma aná­lise de uma entrevista concedida pela ex-ministra Marina Silva ao apresentador Jô Soares em seu talk show. Observo como a entrevistada busca, por meio de recursos verbais e para-verbais, exercer um controle de suas emoções, fundamentando-se mais em argumentos baseados no logos. Para analisar a diversidade plurissemiótica desse discurso, opero com os conceitos de modalização e modulação abordados por Vion (1992; 2003). Entendo o discurso como uma atividade dialógica de pro­dução textual, determinada por fatores históricos e culturais, sempre relacionada a uma situação de comunicação na qual os sujeitos comunicantes exercem papeis sociais, manifestam posicionamentos, assumem uma atitude ativa e responsiva no processo comunicativo: respondem a enunciados anteriores (interdiscursos) e pro­jetam sua comunicação prevendo destinatários potenciais.

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)0.002
metaresearch head score (Gemma)0.008
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0050.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.071
GPT teacher head0.349
Teacher spread0.277 · 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
Published2014
Admission routes1
Has abstractyes

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