Ethnographic information about the Kabardians in Russian sources of the last third of the 18th – first quarter of the 19th century
Bibliographic record
Abstract
The article presents an analysis of some ethnographic information on the Kabardians, recorded in Russian sources of the last third of the 18th – first quarter of the 19th century: office documents, narrative materials, etc. It is shown that they contain contradictory – objective and tendentious assessments of information about the Kabardians. Statements recorded in the sources about the absence of laws and princely property in Kabarda, about the predatory nature of tax collection by princes, etc., as well as the listing of a significant number of negative qualities allegedly inherent in the ethnic character of the Kabardians and other peoples of the North Caucasus, are refuted by information indicating the opposite. In the reviewed sources, demographic and statistical infor-mation is considered mainly in the context of describing the political situation in the region. The positions of the tsarist authorities and the Kabardian political elite on the territorial problem are presented. They recorded a significant decrease in the number of Kabardians by 9-10 times and, as a consequence, a decrease in the military potential of Kabarda. Some sources contain recommen-dations from military officials to eliminate the system of military education of the sons of Circas-sian feudal lords in order to establish full administrative and legal control over Kabarda.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 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".