Designing interaction, voice, and inclusion in AAC research
Bibliographic record
Abstract
The ISAAC 2016 Research Symposium included a Design Stream that examined timely issues across augmentative and alternative communication (AAC), framed in terms of designing interaction, designing voice, and designing inclusion. Each is a complex term with multiple meanings; together they represent challenging yet important frontiers of AAC research. The Design Stream was conceived by the four authors, researchers who have been exploring AAC and disability-related design throughout their careers, brought together by a shared conviction that designing for communication implies more than ensuring access to words and utterances. Each of these presenters came to AAC from a different background: interaction design, inclusive design, speech science, and social science. The resulting discussion among 24 symposium participants included controversies about the role of technology, tensions about independence and interdependence, and a provocation about taste. The paper concludes by proposing new directions for AAC research: (a) new interdisciplinary research could combine scientific and design research methods, as distant yet complementary as microanalysis and interaction design, (b) new research tools could seed accessible and engaging contextual research into voice within a social model of disability, and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".