Caucasian Policy of Russia at the Beginning of the 19th century: the Emperor and Governors
Bibliographic record
Abstract
By the beginning of the 19th century, the vast majority of the peoples of the North Caucasus became part of the Russian Empire, which was recognized by international treaties. The immediate adoption of the Russian administration and orders led to an aggravation of the situation in the Caucasus. Contradictory personality of Alexander I impacted, along with objective factors, to determine the general direction of the Caucasian policy, and personal and professional qualities governors in practice its implementation. Therefore, Caucasian policy of the Russian Empire carried in waves, was not linear. The geopolitical situation of the fi rst quarter of 19th century and the fi rst quarter of the 21st century. Europe has some analogies, the experiences and lessons of the Caucasian policy of Alexander I instructive for the activities of federal districts in the south of Russia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".