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Record W2419021980 · doi:10.3366/tal.2016.0246

At the Limits of Translation? Visual Poetry and Bashō’s Multimodal Frog

2016· article· en· W2419021980 on OpenAlexaff
Mike Borkent

Bibliographic record

VenueTranslation and Literature · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaikuTextualityPoetryMeaning (existential)LinguisticsIconOralitySilenceAffordanceLiteratureArtComputer scienceAestheticsCommunicationPsychologyPhilosophyCognitive psychologyEpistemology

Abstract

fetched live from OpenAlex

This paper discusses several visual poems that both translate and adapt Bashō's popular frog haiku; such works are theorized here as multimodal transaptations. Bashō's poem blends form and meaning, action and stasis, sound and silence. Traditional translations struggle to incorporate some of these features because of the limitations of the target language. Visual poetry, by drawing on the affordances of print – which includes verbal, visual, and diagrammatic cues – is better equipped to reflect and extend the style and content of Bashō's haiku into the more limited forms of English textuality, while also engaging in the playfulness of the haiku and calligraphic traditions. Produced through a hybrid, multimodal approach, these visual transaptations present the reader with a range of cues that are neither strictly mimetic nor adaptive, and which challenge assumptions about stylistic choices, translation, authorship, and poetic meaning.

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)0.004
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.018
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.043
GPT teacher head0.287
Teacher spread0.244 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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