Bibliographic record
Abstract
A number of publications have been devoted to the Persian campaign of Peter the Great and the stay of the Russian Imperial troops on the southwestern shores of the Caspian Sea in 1722–1735 – monographs, articles, dissertations, collections of documents and materials prepared with the use of a wide range of sources and literature. This topic continues to attract the attention of historians even today. It has acquired particular relevance in connection with the 350th anniversary of the birth of the first Russian Emperor Peter the Great and the 300th anniversary of the Persian campaign. A large number of documentary sources from the collections of the federal and regional archives of Russia cover the history of the Persian campaign and its results, which made it possible to reveal new episodes of imperial policy in the Caucasian-Caspian region in the first quarter of the 18th century. Among the most valuable sources on the history of the Persian campaign are the travel notes of the English-speaking authors – the direct participants and eyewitnesses of the events described. One of these sources is the John Bell’s book “Travels from St. Petersburg, across Russia, to different parts of Asia”, particularly, the section titled “Journey from Moscow to Derbent in Persia, in 1722”, translated by the author of the paper into Russian with commentaries. This translation may be a valuable contribution to both the ethnography and historiography of the Russian Caucasian studies of the first quarter of the 18th century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".