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Record W1952577969 · doi:10.1590/2316-4018459

Linguagem, espaço e nação: um mapeamento das identidades multigeográficas do protagonista imigrante

2015· article· pt· W1952577969 on OpenAlexaff
Cecily Raynor

Bibliographic record

VenueEstudos de Literatura Brasileira Contemporânea · 2015
Typearticle
Languagept
FieldArts and Humanities
TopicCultural, Media, and Literary Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Neste trabalho, analiso duas obras que narram a migração: Estive em Lisboa e lembrei de você (2009), de Luiz Ruffato, um romance contemporâneo centrado na história de um migrante mineiro que vai para Lisboa, e Mar paraguayo (1992), de Wilson Bueno, que conta a história de uma prostituta paraguaia sem nome que reside em Guaratuba. Mais especificamente, argumento que os romances de migração com temas contemporâneos desafiam, alongam e/ou interrompem a coesão narrativa do espaço-tempo, obscurecendo, muitas vezes de forma progressiva, as fronteiras entre o nacional e o transnacional. Examino a ideia do espaço tanto no sentido concreto, no âmbito doméstico e o espaço urbano, quanto no sentido metafórico. Dentro deste marco teórico expandido, estendo o argumento espacial ainda mais, postulando que essas narrativas não apenas transgridem ou transcendem os espaços tangíveis, mas também que geram novas ou alternativas possibilidades espaciais a partir das quais podemos observar a construção e manutenção da identidade.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0110.015
Scholarly communication0.0130.010
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.304
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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