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Record W4247772114 · doi:10.1017/s0025100318000129

Central Lisu

2018· article· en· W4247772114 on OpenAlexaboutno aff
Marija Tabain, David Bradley, Defen Yu

Bibliographic record

VenueJournal of the International Phonetic Association · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsChinaTribeGeographyBurmeseEthnic groupQuarter (Canadian coin)Ancient historyEthnologyHistoryArchaeologyAnthropologySociologyLinguistics

Abstract

fetched live from OpenAlex

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 (Chinese yeren ‘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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.282
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

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