Designing An Assistive Naming App for Mandarin-Speaking Anomic Aphasia Patients
Bibliographic record
Abstract
This Major Research Project is motivated by my lived experiences with my grandfather, an anomic aphasia patient, who experienced embarrassment and frustration in his daily life due to lost ability to recollect names of things and people. Starting with a literature review of popular anomia apps and supported by a user persona built based on my grandfather’s profile, I derived a “vocabulary database” in consultation with a speech-language pathologist, and designed a prototype mobile application iRemember, to assist anomic aphasia patients speaking Mandarin and English in recollecting names of daily life objects independently. The app affords searching its database for any real life object by taking a picture using the app and also to add items to the database with audio and text descriptions. The app offers service in Mandarin and English and will be useful to Chinese aphasia patients living in Canada or other English speaking regions who have lost one or both languages. Future work includes developing the prototype into a full-fledged app through a user-centered process, and enabling it in other languages. \n \nKeywords: anomic aphasia, communication, vocabulary, mobile app, assistive technologies, augmentative and alternative communication
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".