Bibliographic record
Abstract
Abstract Augmentative and alternative communication (AAC) systems supplement, but do not replace other modes of communication, such as speech, gestures, vocalizations, or facial expressions. The need for AAC, may be congenital (e.g., cerebral palsy or developmental disability) or a cquired . The severity of need varies from mild to moderate to severe based on physical, cognitive, and linguistic involvement. The overall prevalence from mild to severe for AAC needs is 0.2–0.6% of the total population. There are two basic communication needs that lead to the use of augmentative and alternative communication systems: conversation and graphics. These two needs differ in many important aspects. Conversational needs are those that would typically be accomplished using speech if it were available. The characteristics of AAC devices can be grouped into three major components: (1) control interface, (2) processor, and (3) activity output. The control interface links to a selection method, selection set, and an optional user display. Two selection methods are used in AAC systems. Direct selection is the fastest and easiest selection method to understand and use because the user merely chooses the one that he or she wants. Indirect selection is used to provide access for individuals who lack the motor skills necessary to use select directly and involves one or more intermediate steps between the user’s action and the entry of the choice into the device. Assessment of persons for AAC and training of them to develop skills are very important parts of a service delivery system.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.383 | 0.307 |
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".