Strategies in Construction and Interpretation of Graphic-Symbol Sequences by Individuals who use AAC Systems
Bibliographic record
Abstract
Given the frequent use of graphic symbols in augmentative and alternative communication (AAC) systems, some individuals who use AAC may have greater familiarity with constructing graphic-symbol sequences than do speaking individuals without disabilities. Whether this increased familiarity has an impact on the interpretation of such sequences or on the relationship between construction and interpretation is fundamental to our understanding of the mechanisms underlying communication using graphic symbols. In this study, individuals who use graphic-symbol AAC systems were asked to construct and interpret graphic-symbol sequences representing the same target content (simple and complex propositions). The majority of participants used stable response patterns on both tasks; a minority were inconsistent on both tasks. Asymmetrical patterns (stable on one task but not the other) were rare, suggesting that neither channel (construction or interpretation) preceded the other, in contrast to earlier findings with participants without disabilities (i.e., novice users of graphic symbols). Furthermore, there were differences between stable and less stable responders on measures of syntactic comprehension and cognitive level but not on chronological age, receptive vocabulary, or AAC system characteristics and length of use.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".