Lessons for the AAC field: a tribute to Dr. David Beukelman
Bibliographic record
Abstract
On February 5, 2022, the field of augmentative and alternative communication (AAC) lost a giant when Dr. David "Dave" Beukelman passed away. As the readership of this journal is aware, Dave was one of the principal founders of the AAC field and devoted his career to providing a voice to those without one. Before AAC became a field, people who could not talk were invisible or seldom noticed, unless they were in the way. For more than 40 years, he was a catalyst for change in AAC clinical practice, research, dissemination, teaching, and public policy development. This tribute aims to honor Dave's lifelong mission of serving others by sharing some of his most timeless and valued lessons. Each lesson begins with one of Dave's most enduring quotes that is then followed by a brief synopsis of the lesson Dave hoped to convey.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".