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Record W7075357250

Montréal et São Paulo dans la dynamique narrative de Monique Proulx et Luiz Ruffato

2017· article· fr· W7075357250 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2017
Typearticle
Languagefr
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeESPACESocial analysis
DOInot available

Abstract

fetched live from OpenAlex

Cette communication consistera en une comparaison de deux ouvrages des Amériques, Ce qu’il reste de moi (2015), de Monique Proulx et Tant et tant de chevaux (2002), de Luiz Ruffato. Dans le premier, le Montréal du xxie siècle reste le personnage principal, comme le témoin du destin collectif d’êtres intenses avec des voix multiples qui seraient venues concrétiser le rêve de la « Folle Entreprise » de Jeanne Mance.Des héros migrants méconnus deviennent en effet des conteurs, chargés de maintenir et de transmettre les legs personnels et collectifs dans un espace charnière comme la métropole québécoise. Dans le récit brésilien, le personnage principal comme espace-charnière reste la mégalopole de São Paulo. Ruffato présente 69 micro-récits autonomes et hybrides qui rendent compte de l’errance des laissés-pour-compte, des riches et des gens de la vie ordinaire ; Noirs, Indiens, Nordestins, pauvres, des personnages de toutes origines et conditions sociales.Les deux romans s’inscrivent dans la littérature actuelle où les interrogations sur la place des individus dispersés entraînent une remise en cause de grands récits et d’identités trop définies.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.257
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.007
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.065
GPT teacher head0.400
Teacher spread0.335 · 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
Published2017
Admission routes1
Has abstractyes

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