Rethinking the language development of deaf and hard of hearing children
Bibliographic record
Abstract
Language is a fundamental aspect of communication and meaning-making; it occurs across different codes and modalities, connecting us to diverse communities and environments and expanding our knowledge of their languages and cultures.Language plays a central role in our lives as a means by which we can participate in social interactions, develop thinking skills, and share attitudes and ideas with others.Supporting language development in deaf and hard of hearing children brings a unique perspective to our thinking about language.Many children born with permanent sensorineural deafness experience challenges in acquiring language and learning to communicate, with outcomes generally below those of hearing children.These challenges persist despite increased opportunities for early diagnosis, early intervention, and advanced assistive technology such as hearing aids and cochlear implants.More than 90 per cent of deaf and hard of hearing infants are born to hearing parents, most of whom have had minimal experience understanding the influence of deafness on a child's language development.The language and learning needs of deaf and hard of hearing children are diverse, and there is no 'one size fits all' solution to communication.Therefore, a flexible communication approach sensitive to each child's language strengths is essential if optimal outcomes are to be achieved.Deaf and hard of hearing children have the same potential for language and learning as hearing children if they can access the language of their learning environment.Providing deaf and hard of hearing children and young people with a shared language and opportunities for reciprocal and rewarding communication exchanges is essential for their language development.Whether the shared language is spoken, signed, monolingual or bi/multilingual, a critical factor influencing children's language development is the quality of parentchild interactions and the parents' level of sensitivity to their child's
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".