Il parlait assez bien français et plusieurs langues: Foreign Language Acquisition and the Diplomatic Self-Fashioning of Prince Boris Ivanovich Kurakin
Bibliographic record
Abstract
Using the example of Prince B. I. Kurakin (1676–1727), the Imperial Russian diplomat who served as extraordinary and plenipotentiary ambassador to France (1724–1727), this article seeks to contribute to the ongoing discussion about the possible reasons for the adoption of French as the language of international communication in general and eighteenth-century diplomacy in particular. It asks when the Moscow-born Gediminid prince learned to speak French and how this non-native speaker of the language became proficient enough to impress a finicky and fastidious interlocutor like Louis de Rouvroy, duc de Saint-Simon (1675–1755). The author suggests that the answer to these questions lies not in Russia or France, but in Poland and Italy; and not in the halls of formal educational institutions, but in the networks of personal connections that were sustained as much by face-to-face communication as by written correspondence. This brief biographical survey of the development of Prince Kurakin’s “linguistic personality” demonstrates the mediating role of modern, vernacular languages (Russian, Polish, Italian) in the transition from Latin to French as the lingua franca of international diplomacy. It also emphasizes the intimate connection between foreign language acquisition and diplomatic self-fashioning, showing how linguistic knowledge could be instrumentalized for both personal and professional advancement. In doing so, it illustrates the active role that individual brokers – especially, but not exclusively, aristocratic royal servitors with broad linguistic skills and extensive international connections, like Prince Kurakin and the duc de Saint-Simon – played in creating the very notion of an early modern “European” style of diplomacy based on the cultural dominance of the French language.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".