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Record W4388139660 · doi:10.4000/temoigner.12313

À la recherche des corps perdus. Témoins, témoignages et traumatismes dans Billydéki

2023· article· fr· W4388139660 on OpenAlexaffabout
Kamelia Talebian Sedehi

Bibliographic record

VenueTémoigner. Entre Histoire et Mémoire · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCanadian Association of Physicists
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Au Canada, le dernier pensionnat pour jeunes Aborigènes a fermé ses portes en 1996, mais ses persécutions poursuivent les survivants et leurs descendants. Les œuvres littéraires ont le pouvoir d’informer le public des dérives du passé. Elles mêlent histoire et narrativisation pour faire de la littérature une forme de narration de témoignage capable de présenter les incidents historiques sous un jour nouveau. Les témoignages littéraires transforment ainsi l’histoire des pensionnats canadiens. Billydéki (2019), de Sonia Perron, est un témoignage littéraire des réalités des pensionnats canadiens, mais aussi un parfait exemple de la relation transformationnelle qui lie l’histoire et la narration. Billydéki laisse transparaître les exactions des pensionnats envers les communautés aborigènes. En traduisant en mots l’expérience directe de témoins oculaires, l’autrice historicise ces incidents et abus. La narration ne se contente pas de raconter l’histoire, de dire « voilà ce qu’il s’est passé » ; elle le montre à travers les yeux de divers acteurs ou spectateurs présents au moment du traumatisme. L’objet de cet article est d’appliquer le concept du traumatisme de Judith Herman et le concept de témoignage et de témoin de Shoshana Felman et Dori Laub au roman Billydéki, de Sonia Perron, afin de mettre en lumière les incidents historiques survenus dans certains pensionnats et longuement passés sous silence.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient 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 score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0050.009
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.151
GPT teacher head0.338
Teacher spread0.188 · 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
Published2023
Admission routes2
Has abstractyes

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