Finding Translations. On the Use of Bibliographical Databases in Translation History
Bibliographic record
Abstract
In any study of translations one must first decide what is to be counted as a “translation” and how such things are to be found, usually through recourse to bibliographical databases. We propose that, starting from the maximalist view that translations are potentially everywhere, various distribution processes impose a series of selective filters thanks to which some translations are more easily identified and accessible than others. The study of translation must be aware of these prior filters, and must know how to account for them, and sometimes how to overcome them. Research processes then necessarily impose their own selective filters, which may reduce or extend the number and kinds of translations given by prior filters. We present three research projects where the play of prior and research filters is very different. For one-off large-scale relational hypotheses, the Index Translationum is found to be relatively cost-efficient. For more detailed objects such as translation flows from Spanish into French in a specific period, a book-industry database offers significant advantages. And for a study marked by a paucity of texts, as is the case of translation from Korean into English following the Korean War, a combination of databases is necessary, the most useful turning out to be Amazon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.059 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".