Tyumen District in 1920s: Settlement Numbers and Development Features
Bibliographic record
Abstract
The subject of this study is the rural settlements of the Tyumen district in the first quarter of the 20th century. It is noted that during this period, the Tyumen district was situated at the heart of the Tyumen region, ranking first in terms of population size (44,545 people) and the area of territory covered (5.4 thousand square kilometers). The paper examines changes in the number and typology of settlements within the Tyumen district through the lens of its rural localities. It has been established that the district’s settlement network consisted of 177 localities, falling into 11 types, with villages making up a significant proportion — over 50%. This fact indicates that in long-settled regions, settlement networks have existed in virtually unchanged forms despite various external and internal factors. Fifteen villages were identified as creating the framework of the Tyumen district’s settlement network, demonstrating resilience and successfully adapting to new conditions. For instance, data from 1912 and 1926 show that population numbers in these localities were growing, especially in those settlements occupying advantageous (central) positions within the existing network. Many villages in the Tyumen district attained this status during the Soviet period, even though at the beginning of the 20th century they were mere villages. Successful new connections between settlements were facilitated by transportation factors (the presence of railways, tract roads, and a navigable water artery — the Tura River).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".