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A formação crítica do leitor à reflexão dos fatores pragmáticos da textualidade na produção de fake news sobre a covid-19: uma análise linguística textual

2021· article· pt· W4200344042 on OpenAlexfundno aff
Natália Coêlho Bagagim, Marcelo Silva de Souza Ribeiro, Lucinalva de Almeida Silva

Bibliographic record

VenueScripta · 2021
Typearticle
Languagept
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
FundersFundação Universidade Federal do Vale do São FranciscoUniversidade Federal de AlagoasUniversité du Québec à Montréal
KeywordsFake newsHumanitiesPhilosophySociologyMedia studies

Abstract

fetched live from OpenAlex

Em meio à pandemia do novo coronavírus (Covid-19), a explosão de informações nas mídias a respeito da doença tem intensificado a produção e disseminação de notícias falsas. Dessa forma, este trabalho, apoiado na abordagem qualitativa de pesquisa, objetivou analisar a formação crítica do leitor à reflexão dos fatores pragmáticos da textualidade na produção de fake news sobre a Covid-19. São objetos de análise três publicações sobre a cura do coronavírus mediante insumos caseiros. Essas publicações foram checadas pelo site do Ministério da Saúde e comprovadas como fake news. A pesquisa foi embasada na Linguística Textual sob os fundamentos teóricos de Costa Val (1991; 2008), Koch e Travaglia (1993; 2015), Marcuschi (2008), Koch (2014), Koch e Elias (2008), Rodrigues et al. (2009); Santaella (2018), Leite (2019) e Galhardi et al. (2020), Bauman (2001) sobre fake news e sua intensificação. Constatou-se que a formação crítica dos leitores ainda é limitada, pois os produtores de fake news se valem dos fatores pragmáticos da textualidade para atribuir sentidos ao que se enuncia sobre a Covid-19 e ganham à cooperação do leitor na aceitação dos seus conteúdos. Logo, essa interação autor/texto/leitor dificulta o combate à crise sanitária no Brasil.

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)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.092
GPT teacher head0.389
Teacher spread0.296 · 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
Published2021
Admission routes1
Has abstractyes

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