Assessing communication accessibility in the university classroom: Towards a goal of universal hearing accessibility
Bibliographic record
Abstract
Measures of accessibility typically focus on the physical environment and aspects relating to getting into and out of spaces. The transient sound environment is less well characterized in typical accessibility measures. Hearing accessibility measures can be based upon physical indices or functional assessment. The physical measures are indices that use signal-to-noise ratios to evaluate audibility while the functional assessment tool adopts universal design for hearing (UDH) principles derived from principles of universal design. The UDH principles include (1) Optimization of the hearing environment for all; (2) Optimization of interactions between persons and objects to promote better hearing in an environment; (3) Optimization of opportunities for people to have multiple choices of interactions with one another; (4) Optimization of opportunities for people to perform different activities in and across environments; (5) Optimization of opportunities for people to have safe, private, and secure use of the environment while minimizing distraction, interference, or cognitive loading; and (6) Optimization of opportunities for people to use the environment without extra steps for hearing access during preparatory, use and/or after use phases. This paper compares the two approaches using case examples from post-secondary classrooms in order to describe the potential advantages and limitations of each.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".