Breaking Down Communication Breakdowns in Children who are Deaf or Hard of Hearing
Bibliographic record
Abstract
People with good communication skills are able to express what they want to say and understand what others are saying. Communication can break down when one conversational partner does not correctly perceive, understand, or interpret what the other person has expressed. For example, the following interactions between Daniela and Shanice illustrate a communication breakdown that was resolved after eight conversational turns. 1 Daniela I want a cat but Mom won’t go for it. 2 Shanice What? 3 Daniela Mom won’t go for it. 4 Shanice She won’t [what]? 5 Daniela Like, she won’t do it. 6 Shanice She won’t buy you a cat? 7 Daniela Yeah. She doesn’t want cat hair everywhere. 8 Shanice Oh, well, you can come visit my cat anytime. d/Deaf or hard of hearing (DHH) children are at greater risk of experiencing communication breakdowns than their typically hearing peers. This is not only because reduced hearing abilities make it harder to hear spoken communication and to learn to speak clearly. Many DHH children also experience delayed, distorted, and inconsistent access to auditory and/or visual language over time, which can lead to delayed language abilities. A child needs strong language abilities to identify the part of an utterance that was misunderstood in order to formulate effective requests and repairs.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".