Evenki of River Basin Olyokma: Resettlement and Land Use in the XXI Century
Bibliographic record
Abstract
The relevance of the study is due to the need to study the characteristics of the resettlement of the local Evenki group of the Olekma River Basin, which influenced the modern land use and economic and cultural relations of Evenki living now in the Olekminsky District of the Republic of Sakha (Yakutia), the Kalarsky and Tungiro-Olekminsky Districts of the Trans-Baikal Territory and the Tyndinsky District of Amur region. The article focuses on the study of the Evenki population dynamics based on a comparative analysis of the materials of the censuses of the 20th century. For comparison, current data on the number of Evenks in their areas of residence according to the 2010 census are given. An analysis of archival sources showed that changes in the resettlement of Evenk clans occurred simultaneously with changes in the administrative-territorial division. Factographic materials found in the National Archives of the Republic of Sakha (Yakutia) confirm that when deciding on the administrative-territorial structure, the opinions and proposals of the local population were taken into account. Author’s maps were compiled, which allowed a spatial analysis of the distribution of the population and the forms of organization of the Evenki economy. It was found out that each region has its own specific land use determined by administrative decisions and regional policies.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".