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Record W4285109683 · doi:10.1590/1982-2553202255059

“Pare de sofrer!”: os discursos da Igreja Universal sobre depressão na pandemia da covid-19

2022· article· pt· W4285109683 on OpenAlexaboutno aff
Emanuelle Gonçalves Brandão Rodrigues, Amanda Borba da Silva, Flávia Carvalho de Freitas

Bibliographic record

VenueGaláxia (São Paulo) · 2022
Typearticle
Languagept
FieldSocial Sciences
TopicReligion and Society in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

Resumo A proposta deste trabalho é compreender como a depressão durante a pandemia da covid-19 tem sido discursivizada pela Igreja Universal do Reino de Deus (IURD). Busca-se identificar quais compreensões sobre a depressão e a cura emergem nesses discursos. Para tanto, tomamos como objeto de análise os textos da seção Notícias do portal da IURD. O levantamento e o tratamento do corpus valeram-se de procedimentos da análise de conteúdo proposta por Bardin, para que, em seguida, a amostra fosse analisada discursivamente. A análise se apoiou em estudos de Dunker acerca do sofrimento psíquico na atualidade e de sua relação com o neoliberalismo (DARDOT, LAVAL, 2021). Os resultados apontam que a depressão é discursivizada pela IURD como um problema espiritual, que tem sua cura através da fé, endossando a responsabilidade de si como imperativo da obtenção de resultados, alinhando-se a certos discursos neoliberais.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.040
GPT teacher head0.335
Teacher spread0.295 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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