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Record W4401813466 · doi:10.4324/9781003423096-9

Creative Writing and Translation

2024· book-chapter· en· W4401813466 on OpenAlexaboutno aff
Jonathan Locke Hart

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsTranslation (biology)Creative writingComputer scienceLinguisticsLiteratureArtPhilosophyChemistry

Abstract

fetched live from OpenAlex

Translation and intercultural connections, as I argued in Chapter 7, involve loss and gain and creativity that creates new poetry not simply in connection with the originals. The relation between creative writing and translation is intricate and vital. Here, I explore that connection and then discuss my own experience in both realms. In school and university, I never took a course in creative writing and translation. Still, I became a professor who taught creative writing and gave readings and who was director of a centre that involved creative writing and translation and I have been a chair professor of a School of Translation Studies as well as translating poetry and non-fiction as part of my research and writing. All this was interdisciplinary, and I was trained in history and literature, so beyond teaching English and comparative literature and history, I have also held appointments in a faculty of medicine and in evolutionary biology. Having given readings in Australia, Singapore, Romania, Germany, Slovenia, Poland, China, the United States, Canada, Ireland, the United Kingdom and elsewhere and having lectured in those places and elsewhere in Asia, South America and Europe and remotely in even more countries and having taught in universities in England, France, the United States, Canada and China, I wish, after discussing creative writing and translation, to set out my experience that relates to creative writing and translation. Like many who study foreign languages and who have written poetry and other genres, I experienced this in school before university.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.802
Threshold uncertainty score0.967

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0330.001

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 head0.102
GPT teacher head0.297
Teacher spread0.195 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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