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Record W1790039643 · doi:10.7202/1032636ar

L’engagement et l’écriture (de fiction) à l’épreuve de l’autobiographique chez André Major

2015· article· fr· W1790039643 on OpenAlexaffvenue
Manon Auger, Robert Dion

Bibliographic record

VenueVoix et Images · 2015
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

André Major a été, jusqu’à récemment, connu et reconnu essentiellement pour son oeuvre de romancier et de nouvelliste. Cependant, la parution tardive de trois volumes de ses carnets (Le sourire d’Anton ou l’adieu au roman, L’esprit vagabond et Prendre le large) appelle à une révision de son parcours d’écrivain, permettant en quelque sorte de jeter une lumière rétrospective sur sa production passée. En apparence, ces trois livres ont reconfiguré en profondeur l’oeuvre de Major de deux façons : d’une part, en faisant ressortir une veine autobiographique restée jusque-là plutôt discrète et, d’autre part, en inscrivant une rupture relativement franche avec la pratique de la fiction (que l’on serait tenté de faire remonter à la parution du Sourire d’Anton). Or, il semble que chez Major les réticences à l’égard des contraintes de la pratique fictionnelle apparaissent beaucoup plus tôt qu’on ne l’avait envisagé jusqu’à présent, comme du reste le souci d’une écriture de soi attentive aux exigences de la posture et de la vocation, toujours précaires, de l’écrivain, exigences qui vont de pair avec un désir d’engagement sans cesse menacé par la tentation de la désertion. Cet article montre que c’est la permanence d’une même attitude vis-à-vis divers aspects de sa pratique qui marque avant tout le parcours de Major.

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)0.003
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: none
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.014
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0230.007

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 head0.244
GPT teacher head0.348
Teacher spread0.104 · 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
Published2015
Admission routes2
Has abstractyes

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