Burial rite of the population of the Golden Horde city of Madjar: problems of study and debatable issues
Bibliographic record
Abstract
The paper deals with some issues in the historiography of the burial rite of the population of the Golden Horde city of Madjar. From 1989 to 2020, some small sites in the trade and craft quarter and in the cemetery in Madjar were examined. Recently, craniological, archeozoological and palynological studies have been carried out. Since the beginning of the 2000s, a number of valuable archival materials of the XVIII–XIX centuries have been found and published. In 2021, the boundaries of the ancient settlement were established. Alongside with these achievements, there has been a lag in the study of the burial rite of the city’s population, which was caused by insufficient study of the city’s cemetery. In 2020, I.B. Tishchenko excavated 10 burials in the north-western part of the cemetery, significantly adding to the available source base. In 2022, E.I. Narozhny, I.B. Tishchenko and A.A. Sazonov published a series of articles with the analysis of the burials which had been studied in 2020. The articles contain controversial statements that do not take into account the materials of the research on the Madjar necropolises and some other Golden Horde sites. The Madjar cemetery needs large-scale research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".