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Record W4416033123

Representation of the Lebanese Civil War and its consequences by Lebanese-Quebec writers : distancing, exile, reappropriation. An interdisciplinary approach

2025· article· fr· W4416033123 on OpenAlexaboutno aff
Julie Abi Nader

Bibliographic record

VenueZooKeys · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)Point (geometry)Spanish Civil WarPerspective (graphical)Self representation
DOInot available

Abstract

fetched live from OpenAlex

Cette thèse se propose d’explorer les représentations de la guerre civile libanaise et de ses conséquences dans l’œuvre d’écrivains libano-québécois. Elle s’attache à la question de l’exil, en analysant d’abord la distance que ces auteurs prennent vis-à-vis du conflit, avant d’examiner la manière dont ils se le réapproprient. En mobilisant diverses approches littéraires — notamment la géocritique et les études hétérolinguistiques — cette recherche s’efforce de dégager un fil conducteur, une forme d’identité propre à la littérature libano-québécoise. Nous cherchons également à montrer comment cette littérature parvient à s’emparer d’un sujet aussi conflictuel, oscillant entre une amnésie collective contrainte et une hypermnésie intime, profondément personnelle.La réflexion se concentre, tout particulièrement, sur les effets du temps, de l’espace, du rapport à l'altérité, ainsi que sur l’expérience vécue et les circonstances singulières entourant les personnages littéraires, voire les écrivains eux-mêmes. Se pose ainsi, dès le départ, la question de l’auto-fiction : dans quelle mesure les récits à portée autobiographique sont-ils traversés par des écarts référentiels ? Enfin, nous mettons en lumière le bouleversement des codes littéraires traditionnels, soulignant à quel point ce corpus libano-québécois se construit et se déconstruit sans cesse, affirmant sa modernité au-delà des frontières.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.314
Teacher spread0.293 · 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.

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
Published2025
Admission routes1
Has abstractyes

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