Bibliographic record
Abstract
Hearing loss affects over 1 billion people around the world and is the fifth leading cause of disability. In the United States, approximately 10,000 babies are born each year with significant hearing loss. Although assistive technologies such as cochlear implants (CIs) are available to restore hearing, deaf children who receive CIs on average show significantly poorer language skills and academic outcomes than their normally hearing peers. At the same time, a relatively small percentage of deaf children are born to deaf parents and learn sign language as their first language, and grow up to be excellent, fluent communicators who are bilingual in signed and spoken language. Historically, there has been significant tension between advocates of sign language and “oralists” who discouraged sign language use. This chapter provides a critical review of language development in deaf children, including those with CIs and those exposed to different kinds, and amounts, of signed language. The linguistic and educational outcomes of deaf children are considered in light of current understanding of neurodevelopment, sensitive periods, and neuroplasticity, while highlighting areas of controversy and important directions for future research. The chapter concludes with evidence-based recommendations in favor of sign language exposure for all deaf children.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".