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Record W2989820165 · doi:10.26522/vp.v16i2.2314

Le lieu dans Un vent se lève qui éparpille : du pareil au même ?

2019· article· fr· W2989820165 on OpenAlexaffvenueabout
Suzanne Legault

Bibliographic record

VenueVoix Plurielles · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsYork University
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Cette lecture du roman Un vent se lève qui éparpille de Jean Marc Dalpé tient surtout compte de l’enchâssement des genres littéraires dans le cadre de ce récit. Leur analyse peut nous aider à préciser une des raisons pour lesquelles Marie et Marcel, lorsqu’ils en ont l’occasion, ne quittent pas le lieu du drame. Les personnages évoluent dans le nord de l’Ontario, paysage bien connu de l’auteur. Ce n’est pas pour autant une œuvre uniquement régionaliste. Dalpé voudrait convaincre les lecteurs/spectateurs que peu importe le lieu où habiteraient ses personnages, ils seraient toujours prisonniers d’une variante de la même histoire et ne pourraient s’évader de ce même contraignant, voire angoissant.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.017
Scholarly communication0.0100.007
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.017
GPT teacher head0.229
Teacher spread0.211 · 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 designQualitative
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
Published2019
Admission routes3
Has abstractyes

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