Building the Evidence-Base of Effective Reading Strategies to Use With Deaf English-Language Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nearly 25% of Deaf and Hard of Hearing (DHH) students come from homes where a language other than English is used and are known as English-Language Learners (ELLs). Evidence-based practices used to teach students who are DHH ELLs are imperative. To build an evidence-base, successful strategies must be examined across multiple researchers, sites, and participants. This research is a replication of an effective reading strategy; teaching vocabulary using repeated preteaching sessions paired with viewing American Sign Language books on DVD. Five participants with severe to profound hearing loss participated in this multiple-baseline design (ABC) across three sets of five vocabulary words study. Results indicated that after three sessions of preteaching and viewing the DVD, the majority of participants signed correctly 90% to 100% of the targeted vocabulary. Maintenance data were collected 1 to 5 weeks following the intervention. Implications for practitioners and researchers are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it