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Record W2043224531 · doi:10.7202/1027477ar

Literary Transfer between Peripheral Languages: A Production of Culture Perspective

2014· article· en· W2043224531 on OpenAlexvenueno aff
Ran HaCohen

Bibliographic record

VenueMeta Journal des traducteurs · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHebrewFlemishSubsidyDominance (genetics)ProductivityPerspective (graphical)Distribution (mathematics)Production (economics)GermanLinguisticsSociologyEconomicsPolitical scienceLawPhilosophyArtMathematicsEconomic growthMicroeconomics

Abstract

fetched live from OpenAlex

Literary translations from Hebrew into Dutch and vice versa between 1991 and 2010 are examined as a test case for cultural transfer between two peripheral languages, using a production of culture perspective (Peterson and Anand 2004). The findings show 138 Dutch books translated from Hebrew against 52 Hebrew books translated from Dutch. The data are analyzed by genre, translator’s productivity, and number of books per author. The analysis reveals that both directions were similar in distribution of genres, but differed significantly in translator’s productivity (the productivity of the average Dutch translator is more than twice as high as that of his or her Hebrew counterpart) and in the number of translated titles per author (twice as many in the Dutch market). The discussion traces these differences to the different structure of the translation labour market in Israel as compared to that of the Netherlands and Belgium and to the dominance of Dutch state subsidy and Flemish Community subsidy in both directions of the transfer, however with a different policy of subsidy in each direction. It seems that significant conclusions can be reached by examining such factors as size and distribution of the corpus on the backdrop of labour conditions and state subsidy.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.286
Teacher spread0.238 · 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 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

Citations13
Published2014
Admission routes1
Has abstractyes

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