Personal Names of the Yenisei Qïrqïz of the 9th Century A.D. (Based on Chinese Sources and Old Turkic Runic Writing Monuments of the Yenisei River Basin)
Bibliographic record
Abstract
The paper attempts to identify historical characters of the Yenisei Qïrqïz people of the 9th century documented in Chinese written sources, by reconstructing the pronunciation of the names and titles cited and comparing them with the data of Old Turkic runic writing monuments found on the territory of the Yenisei River basin. The undertaken reconstruction of the Middle Chinese pronunciation builds on the system of the Canadian sinologist E. G. Pulleyblank. Drawing from previous research, the author suggests formal correspondences for eight out of the ten Qïrqïz characters mentioned in Chinese sources in the onomasticon of Yenisei runic epitaphs. For the most part, these correlations rely on personal names matching, either exact or partial, that is, manifested in combinations of individual elements. In some cases, the correspondences between the data in Chinese sources and certain biographical episodes in Yenisei epitaphs suggest full equivalence of the mentioned historical figures. On some occasions, particularly those related to the dating of inscriptions, the arguments are backed by archaeological findings. The author also notes the prospects of dating the Yenisei runic monuments based on an integrated approach to their further exploration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".