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Disposições afetivas no romance Le fou d’Omar, de Abla Farhoud.

2020· article· pt· W3095558568 on OpenAlexaboutno aff
Dionei Mathias

Bibliographic record

VenueInterfaces Brasil/Canadá · 2020
Typearticle
Languagept
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesRomanceSociologyPsychologyArtPsychoanalysis

Abstract

fetched live from OpenAlex

RESUMO: Publicado em 2005, Le fou d’Omar é um romance escrito por Abla Farhoud, escritora canadense de origem libanesa. Narrado a partir de diversas perspectivas, o romance trata de conflitos de família, mas também das dificuldades inerentes ao processo de assentamento no novo contexto cultural do Quebec. Nessa interseção, os afetos se revelam como centrais para a compreensão da dinâmica de vozes e, sobretudo, do acesso à produção discursiva. Afetos, neste contexto, são entendidos como modos de manutenção ou interrupção de relações sociais, incluindo a administração de pertencimento e exclusão. No processo de socialização, o indivíduo aprender a lidar com essas dinâmicas a fim de participar do conjunto de vozes que definem a imaginação das formatações de grupo. O romance de Abla Farhoud representa essas dinâmicas afetivas e mostra como elas são responsáveis pela tomada de consciência da imposição de diferença e sua transmissão. Nesse sentido, este artigo deseja discutir disposições afetivas em dois situações: no microcosmo da família, incluindo relações de amizade e no macrocosmo social de contextos nacionais e internacionais.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.016
Scholarly communication0.0100.004
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0170.002

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.

Opus teacher head0.035
GPT teacher head0.264
Teacher spread0.229 · 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 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
Published2020
Admission routes1
Has abstractyes

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