STUDYING KAZAKH NECROTOPONYMS MONGOLIA AND KAZAKHSTAN (SECOND HALF XIX - FIRST QUARTER OF THE XXI CENTURIES)
Bibliographic record
Abstract
Abstract. The article is devoted to the study of funeral monuments (nektrotoponyms) of the Kazakhs of Kazakhstan and the western regions of Mongolia, where the overwhelming majority of the Kazakhs of this country currently live. Various kinds of expeditions organized during this period collected extensive material on the history, geography, ethnography, as well as toponymy of Mongolia and Kazakhstan. Moreover, in the context of the topic we are considering, it should be especially noted that scientific expeditions and trips of individual researchers were not of a special toponymic nature, but were carried out as part of a general study of history, geography, Mongolia and Kazakhstan. At the same time, the purpose of this article is to analyze the content and main directions of studying necrotoponyms of Kazakhstan and the western regions of Mongolia. When writing the work, the methods of factor and diachronic analysis, as well as the synthesis method, were used. In the course of studying this issue, the author came to the conclusion that the study of Kazakh necrotoponyms was carried out as part of a study of the traditional culture of the Kazakhs, in particular, funeral rites. The collected material shows that in traditional Kazakh society necrotoponyms performed various purely practical and ethnocultural functions. In particular, we are talking about the functions of preserving the historical memory of the population (memory of historical events), orienting people on the ground during their movement across vast steppe spaces, marking the limits of the ancestral territories of the Kazakhs, etc.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".