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Neoliberalismo e mercado místico: o “Despertar” mobiliza o discurso autoempreendedor

2023· article· pt· W4392144638 on OpenAlexaboutno aff
Paula Telles de Menezes Faro, Bruna Luiza de Camillo Allegretti

Bibliographic record

VenueLumina · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicSocial and Economic Solidarity
Canadian institutionsnot available
FundersUniversidade Federal de Juiz de Fora
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Este artigo investiga as aproximações entre o discurso da espiritualidade mística, centrado no significante “despertar”, e o discurso neoliberal, que tece a imagem do “empreendedor de si”. Partindo das críticas ao neoliberalismo de Safatle (2019; 2021), Brown (2019), Dardot e Laval (2016) e Fontenelle (2005; 2017; 2021), e dos estudos de Arjana (2020) sobre o mercado místico, trabalhamos com a hipótese de que o movimento de desenvolvimento pessoal e de investimento em um “capital espiritual” faz parte da racionalidade do “eu empresa” e de sua lógica de autorregulação e autoaperfeiçoamento. Consequência da perda de perspectiva e da insegurança social gerados pelo próprio sistema capitalista, o misticismo moderno, ao buscar uma saída ao sofrimento, acaba por reforçar sua lógica, alimentando o consumo de produtos, de serviços, de um estilo vida e uma identidade espiritualizados. Como metodologia de análise, além da revisão bibliográfica citada, utilizaremos a Teoria do Discurso de Laclau e Mouffe (2015). Como caso concreto, analisaremos como o “Despertar” aparece no discurso vinculado a Sri Prem Baba, guru brasileiro que ficou conhecido por seu método de autoconhecimento chamado de “o caminho do coração” e que foi alvo, em 2018, de denúncias de assédio sexual, abuso de poder e enriquecimento às custas das doações de praticantes.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.015
Scholarly communication0.0090.006
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.076
GPT teacher head0.346
Teacher spread0.270 · 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 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
Published2023
Admission routes1
Has abstractyes

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