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Record W4391238057 · doi:10.4000/cipango.5229

De la peur du multiculturalisme à la valorisation du métissage : l’immigration brésilienne au cœur des discours identitaires japonais

2023· article· fr· W4391238057 on OpenAlexaboutno aff
Pauline Cherrier

Bibliographic record

VenueCipango · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Résumé : Depuis plusieurs décennies, les métis japonais ou hāfu ont investi la scène médiatique japonaise, donnant à voir au reste du monde un Japon ostensiblement multiculturel en apparente rupture avec le mythe, toujours dominant, de l’homogénéité du peuple japonais. Pourtant la société japonaise compte depuis la fin des années 1980 de plus en plus d’immigrés asiatiques, de Brésiliens et de Péruviens nikkei, précisément autorisés à immigrer pour leur origine ethnique commune avec les Japonais. Cet article vise ainsi à analyser comment, à partir du cas spécifique des immigrés brésiliens nikkei, dont une partie est métissée, la valorisation du métissage constitue pour certains d’entre eux un instrument de contournement de l’identité immigrée encore perçue principalement de manière négative. Il s’agit de mettre en lumière les réticences du Japon à se penser comme un pays de diversité, malgré la valorisation d’une minorité de métis au profil ethnique et socio-culturel spécifique, et ainsi de penser la place de la question ethnique au sein des discours identitaires japonais.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0040.002
Open science0.0010.005
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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