On Researching Early Modern Mediated Translations: Challenges and Prospects
Bibliographic record
Abstract
In our first forays into the complex landscape of early modern (English) mediated translation, we have come to identify a number of challenges and obstacles to research in this area, from the difficulty of accessing textual or biographical information, to deeper epistemological and critical biases that seem significantly harder to remedy. What follows is a joint reflection on the main issues that we are currently facing and working to address, and the prospects that research on early modern translations involving multiple textual, linguistic and material mediations may open up for scholars of the Renaissance and beyond. The first, obvious challenge concerns the task of identifying mediated translations. As noted by our colleagues in the Lisbon IndirecTrans research group, this is a general issue but it presents particular problems for the early modern period.1 While catalogues of early modern literary production in various languages certainly exist, and offer crucial information pertaining to the study of early modern texts, mediated or not, translations still often remain relatively invisible as such.2 This is unfortunately the case even with the most recent edition of the Universal Short Title Catalogue (USTC), an otherwise impressive free-access online database combining resources from a vast array of libraries in Europe to offer a bibliographical survey of early modern print culture between 1450 and 1650.3 While translations are technically tagged as such in the USTC, they still remain difficult to identify – perhaps due to the heterogeneous nature of the data and metadata compiled into the catalogue. A simple search using ‘translat*’ as a keyword returns 2176 English titles, while so far the verified number of texts translated into English for the period amounts to more than 6000, according to the Renaissance Cultural Crossroads Online Catalogue of Translations in Britain 1473–1640.4 The difficulties inherent in compiling a cohesive corpus of translations, mediated or otherwise, for a given geographical area, or particular time period, was precisely what inspired the creation of the above-mentioned Renaissance Cultural Crossroads catalogue and its follow-up, Cultural Crosscurrents in Stuart and Commonwealth Britain. An Online Analytical Catalogue of Translations 1641–1660. Both specifically document cases of identified indirect translations and provide as much information as possible as to the mediating texts, languages and translators.
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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 | 0.061 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.020 | 0.062 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".