Altering Practices to Include Bimodal-bilingual (ASL-Spoken English) Programming at a Small School for the Deaf in Canada
Bibliographic record
Abstract
Bimodal-bilingual programs are emerging as one way to meet broader needs and provide expanded language, educational and social-emotional opportunities for students who are deaf and hard of hearing (Marschark, M., Tang, G. & Knoors, H. (Eds). (2014). Bilingualism and bilingual Deaf education. New York, NY: Oxford University Press; Paludneviciene & Harris, R. (2011). Impact of cochlear implants on the deaf community. In Paludneviciene, R. & Leigh, I. (Eds.), Cochlear implants evolving perspectives (pp. 3-19). Washington, DC: Gallaudet University Press). However, there is limited research on students' spoken language development, signed language growth, academic outcomes or the social-emotional factors associated with these programs (Marschark, M., Tang, G. & Knoors, H. (Eds). (2014). Bilingualism and bilingual Deaf education. New York, NY: Oxford University Press; Nussbaum, D & Scott, S. (2011). The cochlear implant education center: Perspectives on effective educational practices. In Paludneviciene, R. & Leigh, I. (Eds.) Cochlear implants evolving perspectives (pp. 175-205). Washington, DC: Gallaudet University Press. The cochlear implant education center: Perspectives on effective educational practices. In Paludnevicience & Leigh (Eds). Cochlear implants evolving perspectives (pp. 175-205). Washington, DC: Gallaudet University Press; Spencer, P. & Marschark, M. (Eds.) (2010). Evidence-based practice in educating deaf and hard-of-hearing students. New York, NY: Oxford University Press). The purpose of this case study was to look at formal and informal student outcomes as well as staff and parent perceptions during the first 3 years of implementing a bimodal-bilingual (ASL and spoken English) program within an ASL milieu at a small school for the deaf. Speech and language assessment results for five students were analyzed over a 3-year period and indicated that the students made significant positive gains in all areas, although results were variable. Staff and parent survey responses indicated primarily positive perceptions of the program. Some staff identified ongoing challenges with balancing signed and spoken language use. Many parents responded with strong emotions, some stating that the program was "life-changing" for their children/families.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".