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Record W2528944787 · doi:10.3406/ranam.2013.1442

The rhetoric of double allegiance : Imagined communities in North American diasporic Chinese literatures

2013· article· fr· W2528944787 on OpenAlexaboutno aff
Deborah L. Madsen

Bibliographic record

VenueRecherches anglaises et nord-américaines · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Les travaux les plus populaires de la littérature nord-américaine chinoise peuvent être lus comme structurellement centrés sur une logique du «ni /ni» : il s’agit de textes comme on peut en trouver dans l’oeuvre d’Amy Tan qui n’affichent de fidélité particulière ni à l’Amérique («le pays d’accueil»), ni à la Chine («la patrie»). Plutôt qu’un témoignage de fidélité double, reposant sur une rhétorique positive d’appartenance nationale du type «à la fois/et», les textes figurant dans ce canon affichent une réponse doublement négative, décrite par Sheng-Mei Ma comme «le baiser de la mort» de l’orientalisme. Cette logique trahit la force résiduelle d’un certain nationalisme racialisé, dans les contextes d’identités hybrides et de formation de communautés en diaspora. Au Canada, le roman de Wayson Choy The Jade Peony et son mémoire Paper Shadows, Disappearing Moon Café de SKY Lee et l’histoire de famille de Denise Chong The Concubine’s Children, par exemple, soulignent l’aptitude des formes littéraires canoniques à cristalliser certaines images de «communautés chinoises de l’étranger», leurs relations avec la «mère-patrie » et la fidélité au «pays d’accueil» ou au «pays de résidence».

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)0.005
metaresearch head score (Gemma)0.006
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 score0.033
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0280.047
Scholarly communication0.0100.007
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.348
Teacher spread0.270 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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