A Consultative Model for AAC/AT Support: A Team Approach
Bibliographic record
Abstract
No AccessPerspectives on Augmentative and Alternative CommunicationArticle1 Jun 2005A Consultative Model for AAC/AT Support: A Team Approach Vicky McKamy, and Jacquelyn R. Moore Vicky McKamy Montgomery County Public SchoolsRockville, MD Google Scholar More articles by this author and Jacquelyn R. Moore Montgomery County Public SchoolsRockville, MD Google Scholar More articles by this author https://doi.org/10.1044/aac14.2.16 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In References ASHA. (1991). Augmentative and alternative communication.Asha, 33 (Suppl. 5), 8. Google Scholar Calculator, S. N., & Jorgensen, C. M. (1991). Integrating AAC instruction into regular education settings: Expounding on best practices.AAC Augmentative and Alternative Communication, 7, 204–214. Google Scholar Fitts, P. M. (1964). Perceptual-motor skill learning.In A. W. Melton (Ed.), Categories of human learning.: New York: Academic Press. Google Scholar Porter, G., & Burkhart, L. J. (2004, October). Designing light-tech & high-tech dynamic auditory scanning systems., Paper presented at the International Society for Augmentative and Alternative Communication (ISAAC), Natal, Brazil. Google Scholar Weitzman, E. (1993, Fall). Remembering to remember: Helping parents and teachers monitor their interactive behavior.: Ontario, Canada: WigWag, The Hanen Center. Google Scholar Additional Resources FiguresReferencesRelatedDetails Volume 14Issue 2June 2005Pages: 16-20 Get Permissions Add to your Mendeley library History Published in issue: Jun 1, 2005 Metrics Topicsasha-topicsasha-sigsasha-article-typesleader-topicsCopyright & PermissionsCopyright © 2005 American Speech-Language-Hearing AssociationPDF downloadLoading ...
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".