Bibliographic record
Abstract
Lisu (ISO 639-2 lis) is spoken by just over a million members of the group of this name in south-western China, north-eastern Burma, northern Thailand and north-eastern India. It formerly also had other names used by outsiders, including Yeren (Chineseyeren‘wild people’), and Yawyin in Burma and Yobin in India (both derived from the Chinese term). Other names included Lisaw from the Shan and Thai name for the group, also seen in the former Burmese name Lishaw. About two-thirds of the speakers live in China, especially in north-western Yunnan Province, but also scattered elsewhere in Yunnan and Sichuan. About a quarter live in the Kachin State and the northern Shan State in Burma, with a substantial number in Chiangmai, Chiangrai and other provinces of Thailand, and a few thousand in Arunachal Pradesh in India. It is also spoken as a second language by many speakers of Nusu, Anung, Rawang and others in north-western Yunnan and northern Burma. Lisu has almost completely replaced Anung in China and is replacing Lemei in China. The Lisu are one of the 55 national minorities recognised in China, one of 135 ethnic groups recognised in Burma, a scheduled (officially listed and recognised) tribe in India, and one of the recognised hill tribe groups of Thailand. Figure 1 shows a map of the area where Lisu is spoken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.329 | 0.182 |
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".