Supporting Families and Professionals to Understand the Role of Hearing Technologies for Students Who Are Deaf or Hard of Hearing
Bibliographic record
Abstract
The use of hearing technology is one of the most effective strategies for providing access to spoken language and the auditory environment for students who are deaf or hard of hearing. In recent years, rapid advancements in hearing technologies have significantly improved access to spoken languages for learners of all ages. As part of the Special Issue “Educating Deaf Students in the 21st Century: A Changed and Changing Context”, this article describes how changes in hearing technology are related to changes in where and how students who are deaf or hard of hearing are educated. This article is designed to provide a foundation of knowledge about today’s hearing technologies for families, educators, and professionals such as speech–language pathologists or early childhood educators who support families and students. It provides an overview of hearing technology options, how they are prescribed and fit, and how benefits for language and literacy development can be evaluated. Barriers to effective use and future directions for hearing technologies are also described. The section “Highlights for Educators and Families” in the article discusses the practical application of this information to the work of those supporting students who are deaf or hard of hearing at home, at school, and in the community.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".