A argumentação no tribunal do júri: emoção e empatia do advogado de defesa em casos de homicídio
Bibliographic record
Abstract
Analisar a argumentação presente na sustentação oral de um advogado que defende um réu homicida e a sua relação com o convencimento dos jurados é o propósito deste trabalho. Dessa forma, fundamenta-se na nova retórica de Perelman e Olbrechts-Tyteca (1996); na sequência argumentativa de Adam (2011), em diálogo com as teorias de emoção, com Plantin (2011) e empatia com Rabatel (2013). Com a finalidade de padronizar a transcrição do corpus, ele foi passado para as normas do Projeto da Norma Urbana Oral Culta (NURC). A análise do dado aponta para os seguintes resultados: a sequência argumentativa seguiu o nível justificativo; sobre o argumento emocional, notamos que foi marcado por lexemas subjetivos; por fim, sobre o argumento empático, verificamos que os mais recorrentes foram a de que L1/E1 se colocar no lugar dos jurados, para evitar arrependimentos futuros, e, o do L1/E1 se colocar no lugar da mãe da vítima.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".