A IMAGEM LITERÁRIA REPRESENTÁVEL E TOLERÁVEL NO DISCURSO DA VISIBILIDADE POLÍTICA DE ANTENOR LAVAL EM DOIS IRMÃOS
Bibliographic record
Abstract
As imagens formam uma nova escrita que modifica os metodos de representacao. Elas surgem como uma nova relacao entre imagem e linguagem, capaz de modificar o legivel e engendrar o visivel. (QUEAU, 1997). Diante disso, articula-se o objetivo deste trabalho: compreender o novo processo de representacao nos discursos de Antenor Laval de Dois Irmaos (2000) de Milton Hatoum, no que se refere a seus pontos de vista sobre a Ditadura Militar no Brasil, tanto na representacao da linguagem literaria no romance, quanto na sua representacao da imagem filmica, adaptada para a minisserie global (2017), roteirizada por Maria Camargo. Para a realizacao dessa analise, partiu-se do metodo de Literatura Comparada (PICHOIS & ROUSSEAU, 2011), correlacionando tal pratica aos aportes teoricos de Literatura e Sociedade (CANDIDO, 2014), imagem e representacao nos textos de Ranciere (2012), discurso da (in) visibilidade politica em Laval a partir de Didi- Huberman (2012), bem como outros autores para sustentar a proposta de que Laval e a personagem que apresenta sua visibilidade politica na realidade ficcional do romance, assim como ganha novos metodos e representacoes, no labirinto das possibilidades interpretativas (LEAO, 2002) por meio das imagens filmicas que corroboram para comprovar a ideia levantada no titulo dessa pesquisa.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".