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Record W2015870869 · doi:10.1353/ari.2014.0030

Multilingual Novels as Transnational Literature: Yann Martel’s Self

2014· article· en· W2015870869 on OpenAlexaboutno aff
Oana Sabo

Bibliographic record

VenueAriel · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMultilingualismPolyglotLinguisticsReading (process)HegemonySociologyTransnationalismIdentity (music)Comparative literatureFlexibility (engineering)AestheticsPoliticsArtComputer sciencePolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Yann Martel’s experimental novel Self (1996) recounts the story of a young man’s gender transformation as he negotiates his national and linguistic identity through cosmopolitan and multilingual affiliations. To convey the tropes of mobility and flexibility, the novel juxtaposes English and several other languages in parallel columns, inviting comparisons across discrete linguistic and literary traditions. Conceptualized from the start as a multilingual novel, Self challenges monolingual ways of classifying national literature and raises questions about plurilingual texts’ placement in literary canons, their implied readers, and their translation into other languages. This article draws on recent debates about transnationalism to read Self as a novel whose formal strategies require a mode of reading predicated on comparison and translation. Readers are encouraged to simultaneously conceive of distinct languages relationally and uncover the hegemonic relationship between global and local languages in Canada and internationally. Through its formal aesthetics, which underscores both the opportunities and limits of multilingualism, Martel’s polyglot novel contributes valuable insights to current discussions of transnational literature.

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)0.002
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 score0.584
Threshold uncertainty score0.828

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.018
Scholarly communication0.0070.003
Open science0.0010.003
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 head0.006
GPT teacher head0.240
Teacher spread0.233 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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