Shopping When You Are Deafblind: A Pre-Technology Test of New Methods for Face-to-Face Communication—Deafblindness and Face-to-Face Communication
Bibliographic record
Abstract
This article presents the first-year results of a project that aimed to explore the feasibility of using a braille display and a smartphone in society to improve face-to-face communication for a person living with deafblindness, using a simulated communication situation. An applied experimental development design was implemented, followed by a pre-test in the community. Two clinicians and an engineer conducted communication tests with three communication partners with normal vision in a shopping mall. A blind clinician acting as deafblind bought an iPhone case and asked for the location of two stores. Communication partners did not report any difficulties, understood the exchanges, and were proud to have helped a person living with deafblindness. No communication breakdowns or keyboard input incidents occurred. Speech turns were not optimal but can be improved. Clinicians proposed a sequence of three training modules: (1) prior knowledge (basic operations for iPhone, software, and braille display), (2) methods for preparing a face-to-face discussion, and (3) processes during a face-to-face discussion. Results demonstrate the feasibility of using a tactile technological solution coupled with a smartphone to interact with unknown interlocutors. Technology trials form the groundwork for a 9-month case study, involving two individuals with deafblindness.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".