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Record W4210791957 · doi:10.1515/lingvan-2020-0016

Conflation of spatial reference frames in deaf community sign languages

2022· article· en· W4210791957 on OpenAlexaff
Oksana Tkachman

Bibliographic record

VenueLinguistics Vanguard · 2022
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsModality (human–computer interaction)ConflationAffordanceSign languageModalitiesSign (mathematics)American Sign LanguageFrame of referenceFrame (networking)Computer scienceLinguisticsManually coded languageFeature (linguistics)Sociolinguistics of sign languagesPsychologyArtificial intelligenceSociologyHuman–computer interactionMathematics

Abstract

fetched live from OpenAlex

Abstract Linguistic spatial descriptions are not purely arbitrary, but are to some extent motivated by many interactive factors. For example, whether the language community is predominantly urban or rural may motivate its reliance on relative or absolute reference frame (Dasen and Mishra 2010; Pederson 1993, 2006). This review paper contributes to Sociotopography in two ways: first, by showing that the distribution of reference frames reported in the literature corresponds to deaf community sign languages and village sign languages (thus the urban-rural differences generalize across modalities), and second, that deaf community sign languages all allow their users to employ a conflated intrinsic-relative frame, which is possibly due to affordances of the visual-manual modality (a modality-specific feature). Comparing the visual-manual and the aural-oral modalities therefore shows that some variation in spatial descriptions correlates with the environment regardless of the modality used, but also highlights modality-specific properties.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.004
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.370
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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