Where do borders lie in translated literature? The case of the changing English-language market
Bibliographic record
Abstract
Anecdotal accounts suggest that one reason for the perceived resistance to translated literature in English-language markets is that commissioning editors are averse to considering texts that they cannot read. In an attempt to overcome this barrier, English translations are increasingly commissioned by publishers of source texts and agents of source authors and used to stimulate interest in a book (not just in English-language markets), a phenomenon this article terms ‘source-commissioned translations’. This article considers how this phenomenon indicates a shift in the borders between literatures, how it disrupts accepted commercial practices, and the consequences of this for the industry and the role of English in the global book trade. In particular, it considers consequences for the quality of translations, questions regarding copyright, and the uncertain position for the translator when, at the time of translating, a contract is not in place between the translator and the publisher of the translation.
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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 | 0.040 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.069 |
| Scholarly communication | 0.041 | 0.047 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".