Velyaminov-Zernov V. V. and His Contribution to the Study of Historical Sources of Moslem Peoples
Bibliographic record
Abstract
The study of trends and outcomes in the development of modern Oriental historiography and source studies involves the study of scientific heritage and contributions of previous generations of scientists. Among those Russian and European orientalists of the XIX century, who made a great contribution to the development of the Russian Science of history and Oriental Studies, a special place is occupied by the Academician Vladimir Velyaminov-Zernov. Until today, the scientific heritage of V. V. Velyaminov-Zernov remained poorly studied. The main purpose of the article is to identify the contribution of V. V. Velyaminov-Zernov to the study of the history of the Turkic peoples and the medieval Muslim Khanates that emerged on the territory of modern Russia after the collapse of the Golden Horde. This article describes the results of research trips and archeographic work of V. V. Velyaminov-Zernov in different periods of his life. The main attention is paid to the Muslim narrative and epigraphic monuments, which were first introduced by him into scientific circulation. Analysis of his work allowed to define the views of Vladimir Velyaminov-Zernov on the continuity of ethno-cultural and religious traditions of the Muslims of the Middle Volga region, southern Urals, Urals, Central Asia and the Middle East.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".