Composite utterances in a signed language: Topic constructions and perspective-taking in ASL
Bibliographic record
Abstract
Abstract Composite utterances are utterances that are built from multiple signs of multiple types, meaning that in any conversational “move” speech, gestures, eye-gaze, intonation patterns, physical stance, etc. all participate in the utterance, and the meaning derived from it is constructed by the composite of these participant types. likewise considers utterances as multimodal ensembles. The present study investigates how the notion of composite utterance plays out in a signed language such as ASL. Articulated in the same modality as are gestures, the distinction between language and gesture has seemed less clear, leading some to ask whether signers even gesture at all and some to suggest that gestures and formal signed language are substantively different systems. On the other hand, others have posited a continuity approach to gesture and signed language especially in light of grammaticalization studies. Here I examine topic-comment constructions and perspectivized clauses in ASL through the lens of Enfield’s composite utterances proposal, looking at component parts and how they function to ground elements in the discourse and guide the interlocutor through the textual structure. I use Enfield’s conventional versus non-conventional type categories in examining lexical and prosodic elements in topic and perspective-taking constructions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".