Análise de discurso religioso: mecanismos acionados por líderes religiosos nas pregações em programas de televisão
Bibliographic record
Abstract
Esta pesquisa se refere a investigacao de um campo surgido na interface entre midia e religiao e tem por objetivos apontar os mecanismos discursivos acionados por algumas liderancas religiosas em suas pregacoes em programas abertos de televisao, alem de compreender as possiveis similitudes e diferencas desses mecanismos. Esse contato entre campos simbolicos aparentemente distintos (FOUCAULT, 1979; BOURDIEU, 1989) permite a reconfiguracao de ambas as esferas, a religiosa e a midiatica (ASSMANN, 1986, CUNHA, 2002, 2007), considerando o sujeito em questao, fiel, inserido na concepcao neoliberal (DARDOT; LAVAL, 2016). Assim, utilizou-se a Analise de Discurso de filiacao Francesa, no que diz respeito, sobretudo, aos conceitos de formacao discursiva, enunciado e cena enunciativa e formacao de sentido (ORLANDI, 1987, 1996, 2005, 2009; MAINGUENEU, 2015) a fim de analisar os referidos discursos proferidos pelos lideres religiosos, quais sejam, Divaldo Pereira Franco, espirita; Valdemiro Santiago, neopentecostal; e Padre Marcelo Rossi, catolico em seus programas, respectivamente, “Conversando com Divaldo”, “culto Dominical do Poder de Deus” e “Santa Missa com Padre Marcelo”. Considerou-se, tambem, os dizeres estruturantes nao-verbais, como gestual, vocal e movimentacao de câmera, no intuito de melhor compreender o discurso em seu contexto televisivo. Os levantamentos apontam para nucleos discursivos em comum, principalmente quando se observam as possibilidades midiaticas, e para certos desvios discursivos, ao se atentar para o carater religioso inerente a cada programa em questao.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".