FOREIGN NARRATIVE SOURCES ABOUT THE EPOCH OF ALEXANDER NEVSKY IN THE WORKS OF RUSSIAN HISTORIANS OF THE 18TH – FIRST QUARTER OF THE 19TH CENTURY
Bibliographic record
Abstract
The article considers the process of introducing foreign narrative sources about the epoch of Prince Alexander Nevsky into Russian historical science. Vasily N. Tatishchev initiated the work with foreign narrative sources for historical research. He took a lot of information from the Byzantine and Latin chronicles to work on “Russian History”. Working on the chronological period of the reign of Alexander Nevsky, he used the works of European travelers (Rubruk, Plano-Carpini, etc.). Tatishchev used foreign sources not so much for critical analysis, but to supplement the data of Russian chronicles. Prince Mikhail M. Shcherbatov continued this process. He used Scandinavian sources in the processing of the Swiss historian P.A. Male. Nikolay M. Karamzin made a large work with foreign narrative sources. He introduced German chronicles into scientific research, such as “The Prussian Chronicle” by Peter from Duesburg, “The History of Livonia” by Christian Kelch, “The Chronicle of Livonia” by Johann Gottfried Arndt, etc. Information from Scandinavian sources became available to him in the book of the Swedish historian Olof von Dalin. Nikolay A. Polevoy used the Chinese chronicles in the retelling of the monk Fr. Iakinf Bichurin, Baron d'Osson and German traveler Yu.G. Klaproth. Russian historians of the 18th century and the first quarter of the 19th centuries actively used information from foreign sources. However, they did not use the original texts, but mostly their retellings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".