A Story of Unspoken Efforts of Elaine Drover \nDesigning Hi-Tech Augmentative & Alternative Communication Systems with Individuals with Cerebral Palsy
Bibliographic record
Abstract
This paper explores the communication challenges and opportunities associated with Augmentative and Alternative Communication (AAC) systems through the lens of Elaine Drover, a woman in her 60s with cerebral palsy (CP) from Newfoundland, Canada. Employing a 'with' rather than 'for' philosophy, this design research project utilizes a three- dimensional framework of inclusive design, which includes recognizing and respecting human uniqueness and variability, using open and transparent processes, and co- designing with those who find current designs challenging. This approach addresses the necessity of designing within complex adaptive systems and highlights the significance of co-design methodologies. By integrating personal narratives and collaborative design, the study investigates the specific needs of individuals with cerebral palsy, aiming to dismantle communication barriers and foster a more inclusive society. The findings emphasize the importance of understanding user needs, goals, and contexts, particularly when developing essential tools that require substantial personal investment. This research not only provides insights into enhancing current AAC systems but also proposes a foundation for future technological innovations in assistive communication, advocating for a shift towards more empathetic and inclusive design practices.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".