Bibliographic Translation Data: Invisibility, Research Challenges, Institutional and Editorial Practices
Bibliographic record
Abstract
In this article, we discuss the main challenges in finding and extracting translation data from national library catalogues and the literary press and propose solutions for researchers to access and analyze bibliographic data for translations. To illustrate these issues, we present two case studies: the first being dedicated to translation invisibility in the literary press, i.e., specialized and general literary journals and magazines, discussing overall trends in the Canadian literary press and giving specific examples from the Quill & Quire and the Montreal Review of Books. The second deals with the institutional practices of collecting and cataloguing translations according to metadata standards at three national libraries: the German National Library (DNB), the Austrian National Library (ÖNB), and the Bibliothèque et Archive nationales du Québec (BAnQ). By doing so, we problematize how the cataloguing, collecting, and reviewing of translated material can be viewed as a systemic issue, highlighting the parallels between these different types of practices. We hope to broaden the understanding of translation invisibility by looking at how institutional, cultural, and editorial practices inform the cataloguing, collecting, reviewing, and publishing of translations.
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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 | 0.409 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.047 | 0.080 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.051 | 0.047 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".