From the Hands into the Eyes: An Analysis of Children's American Sign Language Story Comprehension
Bibliographic record
Abstract
This thesis provides an insight into how native ASL children are making meaning and drawing upon language cueing systems, and into the commonalities of eye behaviour demonstrated when they decipher/deconstruct ASL stories on video. Studies of ASL linguistics and ASL literature are critical, but historically neglected, components of bilingual ASL-English education. While teaching ASL as a language of study in my classroom, I observed how students comprehended ASL stories. In the absence of formal standardized ASL comprehension assessments for classroom teachers, I explored Kintsch's theory of comprehension and the use of language cueing systems as a way to conceptualize ASL story comprehension. Findings show that native ASL children have a higher rate of comprehension when they are drawing upon ASL language cueing systems when deciphering/deconstructing ASL stories. Children's use of the eyebrows and eye gaze seem to be a part of their deciphering/deconstructing behaviour, and need to be explored further.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".