The Kuranty in Context: Dutch Lading Lists and Their Russian Translations. Part 1
Bibliographic record
Abstract
There is a great deal of recent scholarship exploring how foreign news reached early modern Russia and what its impact there was. Of particular importance is the study of the kuranty, the translations of Western newspapers and pamphlets. By examining closely what may seem to have been an unusual choice to translate from Dutch newspapers – the cargo lists of Dutch ships from the East Indies – this article suggests how it might be possible to contextualize the news translations more broadly than has been done to date. It is important to examine the significance of the news where it originally appeared, since its significance in the Russian context may be quite different. And it is also important not just to focus on the Russian government’s interest in the political news that informed its foreign policy. Over a period of decades, the importance given certain topics may have changed. The interests of the translators themselves – among them Andrei Vinius – may help to explain why they selected particular items for translation from the substantial quantity of foreign news which began to arrive in Moscow regularly upon the establishment of the foreign postal connection in 1665. The article is published in two parts, the first one here covering the background and the analysis of the evidence up through 1665. The second part, to appear in a subsequent number of the journal, will deal with the lading lists of 1667 and 1671 and the complex analysis of the context within which they may have been of particular interest in Moscow.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".