Social and academic implications of acoustically hostile classrooms for hard of hearing children
Bibliographic record
Abstract
The correlation between lowered academic achievement and classroom noise has been demonstrated for normally hearing children (Shield and Dockrell, 2003). However, the implications of poor classroom acoustics on the socialization and academic performance of children who are hard of hearing have not been examined. Eleven hard of hearing students in one school district, ranging from kindergarten to grade 7, were the foci of the present study. Acoustic measurements of each of the 11 classrooms in both unoccupied and occupied conditions revealed that all classrooms were acoustically challenging for the hard of hearing students, particularly at transition times, when ventilation was operational, and in the primary grades, when language learning needs are greatest. Interviews with parents and teachers underscored the difficulty these students experienced in comprehending teacher instructions and participating in group work. The students seldom initiated conversation or seatwork independently, but, rather, followed the lead of their peers. The hard of hearing students experienced frequent difficulties in understanding or participating in informal peer-to-peer conversations in the classroom, and parents and teachers attributed the children’s frequent social isolation and withdrawal at school to the combined effects of poor hearing abilities and hostile classroom acoustics. [Work supported by Hampton Research Fund.]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".