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Record W2969530181 · doi:10.3138/utq.88.2.05

Medieval Lyric: A Translatable or Untranslatable Zone?

2019· article· en· W2969530181 on OpenAlexvenueno aff
Ardis Butterfield

Bibliographic record

VenueUniversity of Toronto Quarterly · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryMedieval studiesMedieval literatureLiteraturePower (physics)Medieval historyTranslation studiesHistoryClassicsArt

Abstract

fetched live from OpenAlex

This article argues that the role of Erich Auerbach and Leo Spitzer in debates about ‘world literature’ needs to be re-assessed in the light of their field specialism as medievalists. Hailed as the pioneering founders of the discipline of Comparative Literature, their expertise in multiple medieval languages and literatures has been sidelined. New research into medieval plurilingualism, building on the linguistic skills and interests once taken for granted among these and other key figures of early 20th c medieval studies, affirms the need to understand ‘global’ poetry as being centrally about languages well beyond Anglophone perspectives. Medieval plurilingualism both challenges and extends modern thinking about the global because it heavily engages with rough translation. With examples of untranslatability from the Dominique Strauss-Kahn case to the troubadour Guilhem IX and Chaucer, it is argued that medieval lyric poetry shows the power of untranslatability to disrupt and re-make literary history.

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)0.006
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.021
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.214
Teacher spread0.193 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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