Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".