"It's Not an Issue of Malice, but of Ignorance"
Bibliographic record
Abstract
As video conferencing (VC) has become necessary for many professional, educational, and social tasks, people who are d/Deaf and hard of hearing (DHH) face distinct accessibility barriers. We conducted studies to understand the challenges faced by DHH people during VCs and found that they struggled to easily present or communicate effectively due to accessibility limitations of VC platforms. These limitations include the lack of tools for DHH speakers to discreetly communicate their accommodation needs to the group. Based on these findings, we prototyped a suite of tools, called Erato that enables DHH speakers to be aware of their performance while speaking and remind participants of proper etiquette. We evaluated Erato by running a mock classroom case study over VC for three sessions. All participants felt more confident in their speaking ability and paid closer attention to making the classroom more inclusive while using our tool. We share implications of these results for the design of VC interfaces and human-the-the-loop assistive systems that can support users who are DHH to communicate effectively and advocate for their accessibility needs.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".