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Migration, altérité et espace dans les œuvres d’Abla Farhoud et de Salim Miguel

2015· article· fr· W2281778317 on OpenAlexaboutno aff
Luciana Wrege Rassier

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtESPACECartographyGeography

Abstract

fetched live from OpenAlex

Cet article se propose de réfléchir sur les rapports entre migration, altérité et espace dans les romans Le bonheur a la queue glissante (FARHOUD, 1998) de l’auteure libano-québécoise Abla Farhoud et Nur na escuridão (MIGUEL, 1999) de l’écrivain libano-brésilien Salim Miguel, qui mettent en scène la migration de familles libanaises vers le Canada et vers le Brésil. L’analyse de ces récits porte à la fois sur l’expérience que les personnages ont de l’espace et sur l’espace en tant que matériau pour la construction de ces textes littéraires.Mots-clefs : littérature comparée. littératures migrantes. Espace. Abla Farhoud. Salim Miguel. Abstract : In the following article I propose to reflect on the relationship between migration, otherness and space in the novels Le bonheur a la queue glissante (FARHOUD, 1998) by Lebanon-Canadian Abla Farhoud and Nur na escuridão (MIGUEL, 1999) by Lebanon-Brazilian Salim Miguel, which tell stories of Lebanese families that immigrate respectively to Canada and to Brazil. For that purpose I shall analyze those works considering a dual perspective: how characters experience space and how both writers deal with space as a literary tool.Keywords : Compared Literature. Migrant Literatures. Space. Abla Farhoud. Salim Miguel.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.293
Teacher spread0.250 · 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
Published2015
Admission routes1
Has abstractyes

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