Morphosyntax in Literacy Acquisition Across Languages for Learners Who Are Deaf or Hard of Hearing
Bibliographic record
Abstract
Examining the importance of morphosyntax comprehension in literacy acquisition across languages, this chapter will highlight the bilingual and bimodal advantages some deaf and hard-of-hearing (DHH) learners may possess. Since DHH readers may experience variations in hearing, language, and literacy levels, we examine the interplay of how they develop literacy skills. The complexity of the interplay among the components of reading and writing are considered along with the current research on morphosyntax interventions and assessments for this population of learners. Morphosyntax components that are historically challenging for DHH learners are discussed. Potentially promising practices across morphology and syntax are reviewed, as well as implementations for practice that include an informal assessment designed for DHH learners. Future research necessary to expand our knowledge of how morphology and syntax connect to text is included as a call to action in the quest to improve literacy outcomes for DHH readers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".