The Transformation of Pastoralism in Buryatia: The Aginsky Steppe Example
Bibliographic record
Abstract
Abstract This article deals with the structure of the pastoral economy of East Trans–Baikalian Buryats (Aginsky region). The herd structure used to include the five basic species of domestic animals of Eurasia: sheep, cattle, horses and, more rarely, goats and camels. A horse was of the utmost economic and status significance. However, the quantity of sheep and goats was larger. The pastoral groups owned the land and the nomads migrated with their herds along their traditional seasonal routes. In the last quarter of the nineteenth century, the influence of the Russian economy on the Buryat nomadic economy began to increase. In the USSR, these processes were more intensive. A complete sedenterisation of Buryat society took place. Agriculture was developed and nearly one-quarter of the pastures were used as arable lands. The pastoral economy changed from subsistence to one that was guided by the market. Since wool and meat were valuable commodities new breeds of sheep were raised, and the number of sheep increased greatly thereby giving rise to degradation of pastures. The ecological crisis did not develop on a large scale only because political (collapse of the USSR) and economic crises were ahead of it. As a result, the cattle-breeding and livestock economies of the Aginsky Buryats have fallen into decay. At present, although a crisis situation has been held back, progress is not observed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".