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“VAE, DÁ PRA FAZER!”: Empreendedorismo e Discurso Neoliberal

2023· article· pt· W6954997424 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicHealth, Education, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyNeoliberalism (international relations)CapitalismOrder (exchange)

Abstract

fetched live from OpenAlex

Este estudo propõe uma análise linguístico-discursiva do jingle “Vamos Ativar o Empreendedorismo (VAE)”, veiculado pela Rede Globo desde 2020. O projeto comercial circula em espaço televisivo do Brasil e no meio digital, propagando a ideologia neoliberal em uma das maiores emissoras brasileiras. Discorremos sobre o neoliberalismo e sua ideologia a partir da perspectiva marxista, por meio de estudos de Saad Filho (2015), Boito Jr (1999), Harvey (2005) e Dardot e Laval (2016), ressaltando a centralidade que o empreendedorismo assume em seu discurso. A ideologia neoliberal rejeita de modo abstrato e seletivo a participação do Estado na economia. Assim, o discurso do empreendedorismo, baseado na livre-iniciativa e no mercado, surge como contraponto à ação estatal, colocando no âmbito individual a solução para problemas sociais complexos, como o desemprego e a miséria. Em ordem de refletir sobre esse discurso enunciado, amparamos a investigação em Charaudeau (2010, 2011), especialmente nos estudos do linguista do discurso sobre linguagem política e linguagem midiática. Ainda que seja uma propaganda veiculada pela imprensa brasileira, a ideologia neoliberal se faz presente em praticamente todo o mundo, variando de acordo com cada realidade concreta. Amparada nessa ideologia, a política neoliberal teve como resultado o aumento da desigualdade e da precarização do trabalho, mas segue hegemônica. Buscamos, assim, refletir sobre o papel do discurso na sua legitimação.

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)0.004
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0040.001
Scholarly communication0.0030.005
Open science0.0050.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.000

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 head0.384
GPT teacher head0.609
Teacher spread0.225 · 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 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
Published2023
Admission routes1
Has abstractyes

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