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Memorial Traces as Tropes of Postcolonial Hauntings in Robert Lalonde’s Sept Lacs plus au Nord and Nina Bouraoui’s Mes mauvaises pensées

2018· article· en· W2901854014 on OpenAlexaboutno aff
Jasmina Bolfek-Radovani

Bibliographic record

VenueLondon Journal of Canadian Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicFrench Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsFrenchTrope (literature)NarrativePoeticsContext (archaeology)TRACE (psycholinguistics)Reading (process)AmbiguityLinguisticsArtSociologyLiteratureHistoryHumanitiesPoetryPhilosophyArchaeology

Abstract

fetched live from OpenAlex

This article is a comparative analysis of the language of memory in two auto-fictional narratives by two postcolonial francophone authors of mixed background, belonging to the area of Québec (Robert Lalonde) and Algeria (Nina Bouraoui). It will be argued that both authors seek to deconstruct the binary relationship of the spaces and identities they each belong to (white-Amerindian for Robert Lalonde vs. Franco-Algerian for Nina Bouraoui) through a specific poetics of writing or language of memory. At the same time, they each return cyclically in their writing to the postcolonial spaces, memories and histories of their respective non-Western cultures, as if ‘haunted’ by these spaces. Using the method of close textual reading in a comparative postcolonial francophone context, the article aims to show how the language of memory is deployed in the two narratives chosen. It demonstrates that both authors use the figure of the memorial trace as a trope of haunting in order to construct that language. It concludes that the figures of memory identified in the two texts analyzed give rise to a series of ‘postcolonial hauntings’ producing a postcolonial discourse of ambiguity rather than resistance.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.258
Teacher spread0.234 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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