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Record W4234323759 · doi:10.22215/etd/2015-10838

Investigating disjunction in American Sign Language: The importance of nonmanual signals and the influence of English

2015· dissertation· en· W4234323759 on OpenAlexaff
Olivia Podlesny

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyAmbiguityLinguisticsPreferenceInterpretation (philosophy)Mathematics

Abstract

fetched live from OpenAlex

This thesis investigates the importance of nonmanual signals (facial expressions, movements of the head and body) in American Sign Language (ASL), and argues that nonmanual signals are the overt realization of logical operators. The empirical focus is on disjunction. First, we explore the effect of English influence on nonmanual signals, using Kidd's (2010) theory of the displacement of elements, to account for the pervasive influence of English on the language of native signers. Second, we examine native signers' interpretation of a nonmanual coordinator (a shifting of the body from one side to the other) that is ambiguous between inclusive-disjunction/conjunction, and may be disambiguated with additional nonmanual signals; head nod for conjunction, squint/furrowed brow for disjunction We find a mismatch between linguistic competence and linguistic performance in delayed first language learners, and a preference for a conjunctive interpretation by native signers when there is a lack of disambiguating cues. We show that this preference is part of a general pattern in populations with the inclusive-disjunction/conjunction ambiguity, such as adult speakers of Warlpiri

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.664

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 head0.019
GPT teacher head0.343
Teacher spread0.324 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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