Finding Translations. On the Use of Bibliographical Databases in Translation History
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 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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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