Topographic Landscape Of The Xviii Century: The Case Of Astrakhan Province
Bibliographic record
Abstract
The article is devoted to the study of toponyms recorded in the archival documents dating back to 1742. The material attracted our attention because it is a document-based description of the activity of Tatishchev at the time when he used to be the head of the Kalmyk Commission at the Collegiate of Foreign Affairs and the Governor of the Astrakhan province. These documents are stored in the National Archives of the Republic of Kalmykia and they are undoubtedly important for studying the toponymic space of the Lower Volga Region. The aim of the study is to reconstruct the toponymic landscape of the Astrakhan province according to the route, known as a “military campaign” from Astrakhan to Tsaritsyn. Based on the study, the authors came to the conclusion that the toponyms recorded in the archival documents can be divided into 3 groups. Along with the original Russian terminology, the toponymy of the Lower Volga Region uses nomenclature terms of Turkic origin. Substrate toponyms found in the toponymy of the region are also encountered in a number of modern toposystems of the Russian regions and the eastern regions of Ukraine. The authors believe that the analysis of onomastics of the second quarter of the XVIII century based on the region example will be a new step in the development of historical onomastics in general. The analysis of the identified toponyms recorded in the archival documents can be of interest not only for linguists but also for geographers, ethnographers and cultural experts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".