ASL sign lowering as undershoot: A corpus study
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".