Altaic Lexical Elements in the <i>Slovo o polku Igoreve</i> and the Sceptics
Bibliographic record
Abstract
The Slovo contains some 45 archaic words which are Altaic (Turkic) borrowings, most of which refer to the Cumans (polovci), since the epic describes the military campaign of 1185 AD against them. These archaic lexical elements in the text of the Slovo have always been an intractable problem for sceptics who have denied its antiquity and its authenticity As a rule, the sceptics disregarded them. A. Mazon offered an ingenious, yet infelicitous explanation for their presence in the Slovo, claiming that they may have been imported by Tatar catechumens. A. A. Zimin attempted to deny the antiquity of the Turkic lexical content of the Slovo. In his book, B. L. Keenan argues that words in the Slovo, identified as Turkic borrowings, are, in reality, “ghost-words”, or are words which Josef Dobrovský invented or interpolated into the Slovo from different languages. Keenan acknowledges that in addition to toponyms and proper names of Turkic origin, the Slovo contains some Turkic loan words, but these, according to Keenan, are so few, and well known, that they may be considered irrelevant by the sceptics and may be disregarded. T. Fefer maintains that several Turkic lexical borrowings in the Slovo represent references to the opponents of Feofan Prokopovič. The present article critically surveys these hypotheses from the viewpoint of lexicology
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".