The Kokand Tradition of Bayaz Manuscript Compilation and its Distinctive Features
Bibliographic record
Abstract
When bayaz entered Central Asia as a literary source, the demand for it gradually increased. Unlike the other collections of this type, bayaz quickly gained fame because it consisted of a “bouqet” of poets’ works and began to become a popular from the XIXth century. As a result of development, bayaz schools were formed within each khanate while these schools retained the common features of bayaz, each school also began to acquire its own characteristics which are important distinguishing features between them. This situation became especially noticeable in the second half of the XIXth century. Until this period, the bayazes, which had been composed almost identically in the three khanates, gradually began to change in their structure, artistic decoration and content. Along with the creation of traditional bayazes, new types of bayazes were also created, which requires researchers to seriously study the schools of bayazes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".