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Record W2944894853 · doi:10.7202/1059513ar

SAISIR LE PASSÉ DANS LE PRÉSENT

2019· article· fr· W2944894853 on OpenAlexvenueaboutno aff
Marion Kühn

Bibliographic record

VenueVoix et Images · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Le motif de la filiation sert, comme l’ont démontré les travaux de Dominique Viart, puis de Laurent Demanze, en littérature contemporaine, le plus souvent à exprimer les enjeux d’une transmission défaillante de la mémoire familiale. Sujet au coeur de multiples romans et récits contemporains au Québec, la tentative d’appropriation fictionnalisée d’un passé familial inconnu relève, chez Anaïs Barbeau-Lavalette, Carole David et Judy Quinn, de ce qu’Astrid Erll appelle une « mémoire transculturelle », car c’est un passé autre qui fait irruption dans le présent des personnages. Construits autour de départs de membres de famille, les fictions des trois écrivaines imbriquent en effet mémoire familiale et histoire internationale en embrassant soit le point de vue en amont, soit celui en aval de la lignée familiale. Par le truchement de dispositifs narratifs et temporels complexes, qui servent de point de départ pour l’analyse, les fictions mettent à l’avant-plan le rôle des personnages comme porteurs d’une mémoire qui s’avère stratifiée. Dégageant les différents rapports au temps qui sous-tendent les mises en scène de mémoires « voyageantes », cet article fait l’analyse de la variété des postures proposées par ces fictions québécoises contemporaines face au « présent [qui] s’[est] découvert inquiet, en quête de racines, obsédé de mémoire » (Hartog 2012 : 248).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.248
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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