Scoping review of research methodologies across language studies with deaf and hard-of-hearing multilingual learners
Bibliographic record
Abstract
Abstract In recent years, research interest in both multilingual learners and, more specifically, in immigrant populations has increased. This is also true for students who are d/Deaf and hard of hearing (d/Dhh) and have families who do not speak or sign the languages of the wider community at home (d/Dhh multilingual learners; DML), and may be recent immigrants transitioning to a new country (immigrant DML; IDML). This is a low-incidence, diverse population of learners with minimal research on both language development and on adequate language support during the schooling years. The present study is a scoping review of the research methodologies utilized to conduct 33 original studies. The results help to explain why research is both lacking and sorely needed, and provide a basis for researchers to identify desiderata in research foci and research designs. Recommendations for educational research with DMLs are proposed.
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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 | 0.122 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.058 | 0.057 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".