Bibliographic Translation Data: Invisibility, Research Challenges, Institutional and Editorial Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it