Acoustics and sociolinguistics: Patterns of communication in hearing impairing classrooms
Bibliographic record
Abstract
In elementary school classes, noise during student led activities is often taken as evidence of successful interaction and learning. In this complex social environment of elementary school classrooms, acquisition of complex language and social skills—the focus of activities in early education—is expected to take place in hearing-hostile environments. Communication and language processing in these contexts requires interactive strategies, discourse forms, and syntactic structures different from the educationally desired forms used in acoustically advantageous environments. Recordings were made of the interaction of groups of students in grades 1–3, 5, and 7 during collaborative group work in their regular classrooms. Each student wore microphones at the ear level and head-mounted video cameras. Each group as a whole was also audio- and videotaped and noise level readings were recorded. Analysis of the acoustical and phonological properties of language heard by each student has demonstrated that the language variety used in these noisy and reverberant settings is similar to that of individuals with hearing impairments. This paper reports similarities between the syntactic structures and pragmatic strategies used by hearing impaired children and normally hearing children in noisy contexts. [Work supported by Peter Wall Institute for Advanced Studies, University of British Columbia.]
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".