Investigating disjunction in American Sign Language: The importance of nonmanual signals and the influence of English
Bibliographic record
Abstract
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 (Davidson, 2013).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 (Bowler, 2014) and English-speaking children (Singh et al., 2013).First and foremost, I would like to thank Carleton University SLaLS Faculty ASL Expert Jon Kidd, who has been my mentor for the last eight years, and has made a tremendous impact on my life.I am forever indebted to you for your wisdom and experience, which you selflessly shared during countless hours spent at Mike's Place.I would especially like to thank ASL Consultant Denise Wilton, for lending her talent, time, and energy to this project, and my participants, without whom this research would not have been possible.I would also especially like to thank my supervisor, Dr. Raj Singh, for his endless patience and support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".