Data, Discoverability, and Translation in the UK and Irish Book Markets
Bibliographic record
Abstract
This article explores limitations upon available data on translated books in the UK and Ireland, focusing on the absence of a comprehensive database of titles. Drawing on our investigations and on interviews with publishing professionals, we outline the information gaps and institutional practices that hamper empirical research. We focus on a technical issue that is linked in direct ways to the visibility of writing in translation and translators: namely, the creation and circulation of bibliographical metadata, and the underlying infrastructures through which metadata is shared. We suggest that the discoverability of books in translation—in terms of identifying them as being translated—is directly linked to the extent to which translation and the work of translators are valued by publishers. We then go on to develop this link with respect to literary translators, showing how an absence of information and transparency in the industry defines their working conditions.
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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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.027 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
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".