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Suzanne Jacob entre o lirismo de suas canções e uma narrativa autobiográfica: as personagens Anna e Flore dialogando com “Ana”, de Clarice Lispector

2014· article· pt· W1831581088 on OpenAlexaboutno aff
Ady Sá Teles Santana

Bibliographic record

VenueBabel. · 2014
Typearticle
Languagept
FieldArts and Humanities
TopicLiterature, Culture, and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Este artigo é dedicado a analisar as personagens "Anna" e "Flora" de Suzanne Jacob, escritora do Quebec (Canadá) dialogando com a personagem "Anna" de Clarice Lispector, escritora brasileira, de tal modo observa-se-á as conexões entre a vida e a obra da escritora canadense. Neste sentido, faz-se um estudo da produção discográfica e literária de Suzanne Jacob, mais especificamente do seu romance "Flore Cocon" e do álbum "Une humaine ambulante", cujos personagens de Jacob fazem parte. Para tanto, investiga-se o perfil dessas personagens em relação a "Ana" do conto “Amor” de Clarice Lispector. Dessa forma, o principal objetivo deste trabalho é a comparação entre a vida e obra da escritora, através da análise das características de cada uma dessas representações que personificam Jacob. Estudando os relatos sobre a vida de Jacob e de como a obra simboliza a forma de viver dessa escrita, constata-se que há uma imbricação entre vida, narrativa e personagens. Assim, o conceito de identidade está presente nesta análise, utilizando as definições de “sujeito de Stuart Hall (2005). Os estudos de literatura comparada são de grande valor para essa análise, pois a literatura comparada é o principal elemento aqui analisado. Logo, constata-se que a arte literária e a música representam Suzanne Jacobe através de Anna e Flore, e Ana do conto “Amor” representa Clarice Lispector.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, 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 score0.483
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.231
Teacher spread0.216 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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