Bibliographic record
Abstract
In spoken languages, disfluent speech, narrative effects, discourse information, and phrase position may influence the lengthening of segments beyond their typical duration. In sign languages, however, the primary use of the visual-gestural modality results in articulatory differences not expressed in spoken languages. This paper looks at sign lengthening in American Sign Language (ASL). Comparing two retellings of the Pear Story narrative from five signers, three primary lengthening mechanisms were identified: elongation , repetition , and deceleration . These mechanisms allow signers to incorporate lengthening into signs which may benefit from decelerated language production due to high information load or complex articulatory processes. Using a mixed effects model, significant differences in duration were found between (i) non-conventionalized forms vs. lexical signs, (ii) signs produced during role shift vs. non-role shift, (iii) signs in phrase-final/initial vs. phrase-medial position, (iv) new vs. given information, and (v) (non-disordered) disfluent signing vs. non-disfluent signing. These results provide insights into duration effects caused by information load and articulatory processes in ASL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".