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Record W2520645543 · doi:10.21992/t92h02

Translation, Littérisation, and the Nobel Prize for Literature

2016· article· en· W2520645543 on OpenAlexvenueno aff
Kelly Washbourne

Bibliographic record

VenueTranscUlturAl A Journal of Translation and Cultural Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPrestigeContext (archaeology)Translation studiesNeutralityLiteratureSociologyHistoryEpistemologyArtLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This work is a cultural economics study of the problem of translation production and assessment in and leading up to the literary Nobel Prize deliberations. I argue that the constraints of assessing an unevenly and partially translated body of literary works, many of them from less common languages, present an unbreachable expertise gap. Translation as a sacralization, or consecration in Casanova’s (2004) term, of a writer’s work is considered in the context of the award. Ultimately the prize is shown to depend upon translations carried out in dissimilar circumstances for each candidate. The award of the Nobel is part of the founder’s call for works to be more widely circulated, not to reward fame; thus a Nobel is more an invitation to translate than a recognition of an author in translation, although evidence suggests that the post-Nobel translational impact may vary by writer and over time. This study sheds light on the degree to which the Prize is an authority-mediated phenomenon, and while critiquing the quixotic task of judging disparate forms and amounts of cultural capital side by side, and never from a point of neutrality, it also attempts to show how translation shapes this symbolic form of prestige in the struggle for existence. I posit that alternative prizes and prize-awarding in general as fraught with similar cross-language challenges. Possibilities for future research, qualitative analysis of the Nobel and translation demand, among other consequences, are briefly sketched.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.345
Teacher spread0.271 · 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 teacher head, 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

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