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Record W4389998210 · doi:10.7202/1108160ar

Les défis de la « décontextualisation » : la lecture comme processus digestif dans Péristaltisme d’Éric Charlebois

2023· article· fr· W4389998210 on OpenAlexaffvenueabout
Ariane Brun del Re

Bibliographic record

VenueFrancophonies d Amérique · 2023
Typearticle
Languagefr
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsHumanitiesPhilosophyMathematicsArt

Abstract

fetched live from OpenAlex

Cet article compare les deux premiers recueils de poésie du Franco-Ontarien Éric Charlebois à partir des travaux de Bertrand Gervais sur les régimes de lecture afin d’explorer les limites lectorales de la littérature minoritaire dite « universaliste » (ou « décontextualisée »). Il montre comment Faux-fuyants (2002), un recueil de poésie « particulariste » (ou « surcontextualisé ») ancré dans le Nord de l’Ontario français, permet aux lecteurs et lectrices qui connaissent la littérature franco-ontarienne de choisir entre ce que Gervais nomme la lecture-en-progression et la lecture-en-compréhension. En revanche, Péristaltisme : clystère poétique (2004) aborde un thème universel, la digestion, tout en mettant constamment en échec ces deux régimes de lecture. Plutôt que de pouvoir choisir leur régime de lecture, les lecteurs et lectrices sont mis au régime par la lecture qui, en se révélant « indigeste », risque de les laisser sur leur faim. Dans l’ensemble, cette étude comparative montre que l’universalisme n’est pas nécessairement la solution aux défis de lecture que le discours critique sur les littératures minoritaires associe souvent au particularisme.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.221
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.014
Scholarly communication0.0080.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.261
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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