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Native American Signed Languages

2017· book-chapter· en· W2731249616 on OpenAlexaboutno aff
Jeffrey E. Davis

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsLingua francaIndigenousIndigenous languageVariety (cybernetics)American Sign LanguageManually coded languageFocus (optics)Sign languageNative americanHistoryComputer scienceEthnologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This chapter highlights the linguistic study of Native American signed language varieties, which are broadly referred to as American Indian Sign Language (AISL). It describes how indigenous sign language serves as an alternative to spoken language, how it is acquired as a first or second language, and how it is used both among deaf and hearing tribal members and internationally as a type of signed lingua franca. It discusses the first fieldwork carried out in over fifty years to focus on the linguistic status of AISL, which is considered an endangered language variety but is still used and learned natively by some members of various Indian nations across Canada and the United States (e.g. Assiniboine, Blackfeet/Blackfoot, Cherokee, Crow, Northern Cheyenne, Nakoda/Lakȟóta, and Mandan-Hidatsa). The chapter also addresses questions of language contact and spread, including code-switching and lexical borrowing, as well as historical linguistic questions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.041
GPT teacher head0.296
Teacher spread0.255 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2017
Admission routes1
Has abstractyes

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