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Record W7039281529

L’Hybridité des personnages dans le roman Comment faire l’amour avec un nègre sans se fatiguer de Dany Laferrière

2021· article· fr· W7039281529 on OpenAlexaboutno aff

Bibliographic record

VenueDalarna University College Electronic Archive · 2021
Typearticle
Languagefr
FieldComputer Science
TopicWeb Application Security Vulnerabilities
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral interestEthnic communityCultural environment
DOInot available

Abstract

fetched live from OpenAlex

L’identité est un concept très complexe qui peut se décliner en quatre catégories : l’identité ethnique, l’identité culturelle, l’identité sociale et l’identité personnelle. Alors que l’identité ethnique sera difficilement changée, les trois autres catégories peuvent muter, peuvent se combiner à d’autres éléments, on parle d’hybridation de l’identité. L’identité hybride est une identité ayant plusieurs composantes et se présente en fonction de la situation vécue par une personne. Dany Laferrière nous montre une manifestation de cette identité hybride dans son ouvrage Comment faire l’amour avec un nègre sans se fatiguer. L’hybridité est constatée chez les jeunes filles blanches fréquentant les personnages noirs issus de la migration à Montréal. Elles bravent des interdits pour passer le temps avec ces personnages quitte à avoir une double vie, témoignant alors de la multiplicité de leurs identités en fonction de la situation.L’identité unique n’existe plus, mais pourra se manifester à l’issue d’interactions répétées formant alors une nouvelle identité. L’interculturel est alors à la base de l’identité hybride. Cette dernière ne pourra cependant être qu’éphémère.

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)0.003
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 score0.175
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.017
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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
Published2021
Admission routes1
Has abstractyes

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