Bibliographic record
Abstract
This article provides a bilingual perspective about literacy development in deaf students and uses the bilingual perspective to recommend effective teaching strategies for this group of students with special needs. In the case of deaf students, however, the bilingualism is not between two oral languages, but between American Sign Language (ASU and written English. The analogy of Deaf education to bilingual education is imperfect, as the article shows, but nonetheless helpful in suggesting educational strategies. One difference from classic bilingual education is the difference in mode of the two languages, with ASL using a haptic mode (signing) and written English using a visual mode. Another difference is the nontraditional nature of Deaf communities. Although ASL communities certainly have histories and traditions, Deaf individuals rarely learn these from family ties or immersion in a kinship-based culture that "speaks" ASL. Despite these differences in language mode and cultural transmission, teaching deaf students benefits from many strategies usually associated with the teaching of second languages, including fostering motivation, developing self concepts, understanding language development, knowing elements of a student's first language, allowing judicious translation,focusing on comprehension rather than syntax, and incorporating cultural values and native speakers-signers as role models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".