Bibliographic record
Abstract
The Bai language ( ) is spoken by approximately 1.6 million people in northwest Yunnan Province, China. Of the 25 minority languages spoken in Yunnan, where 33% of the population are ethnic minorities and 67% are Han Chinese, the Bai ethnic minority is second in population only to the Yi (Wiersma 1990, 2003; 2010 census). Bai is classified as a Tibeto-Burman language (Xu & Zhao 1964, 1984), although arguments have been raised as to its possible early Sinitic origins (Starostin 1994, 1995). A summary in French reviews Chinese loanwords, ancient Bai, and comparative Bai dialects (Dell 1981). The historical influence of Chinese on Bai has been significant, but evidence is not compelling that Bai is Sinitic (Norman 2003: 73). There are three major dialects of Bái: Jiànchuān ( ), Dàlĭ ( ), and Bìjiāng ( ). The data in this illustration represent the variety of Jianchuan (jian1239, BCA). The third author ( ), who was about 60 years old at the time of recording, is a male native of the Jianchuan region, originating from QiÁohǒu, a mountain village some 50 km southwest of Jianchuan city – a remote area known for salt mining and where the language has been less influenced by modern Chinese. These locations are indicated on the map of Yunnan (the southwesternmost province of China in an intensely minority-language-populated area) in Figure 1. The traditional geographical link from Qiaohou is to Jianchuan to the north rather than to Dali to the south, and many of the most distinctive characteristics of Jianchuan Bai described here are not found in Dali Bai.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
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".