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Record W1992808831 · doi:10.1515/labphon.2011.015

ASL sign lowering as undershoot: A corpus study

2011· article· en· W1992808831 on OpenAlexaff
Kevin Russell, Erin Wilkinson, Terry Janzen

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2011
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSign (mathematics)Perspective (graphical)Categorical variablePsychologyLinguisticsForeheadPhenomenonNatural (archaeology)Cognitive psychologyComputer scienceHistoryArtificial intelligenceMathematicsSociologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract American Sign Language (ASL) signs that are located on the forehead in their canonical form are often articulated lower during natural signing. Previous studies have examined this phenomenon from a phonetic perspective, treating it as a form of undershoot, and from a variationist sociolinguistic perspective, treating it as a categorical process. This study sees if the findings and explanations of these studies can be extended to the lowering of signs formed in locations other than the forehead. In a corpus of natural conversational signing from six signers, we measure the vertical displacement of over 3000 tokens of signs canonically formed at the face, head, or neck. While there is some apparent evidence for a categorical lowering process in a minority of signs and considerable evidence for undershoot, neither alone can explain the full range of displacement patterns across all signs and locations. Undershoot must be carefully planned and controlled: no matter how sloppy the signing, signers systematically avoid contacting their eyes. The results can be explained if there is a somatosensory forward-modelling mechanism that can veto undesirable gestural scores and whose decisions are incorporated during learning into the phonological distributions representing the locations of signs.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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 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

Citations25
Published2011
Admission routes1
Has abstractyes

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