An Investigation of Developmental Changes in Interpretation and Construction of Graphic AAC Symbol Sequences through Systematic Combination of Input and Output Modalities
Bibliographic record
Abstract
While research on spoken language has a long tradition of studying and contrasting language production and comprehension, the study of graphic symbol communication has focused more on production than comprehension. As a result, the relationships between the ability to construct and to interpret graphic symbol sequences are not well understood. This study explored the use of graphic symbol sequences in children without disabilities aged 3;0 to 6;11 (years; months) (n=111). Children took part in nine tasks that systematically varied input and output modalities (speech, action, and graphic symbols). Results show that in 3- and 4-year-olds, attributing meaning to a sequence of symbols was particularly difficult even when the children knew the meaning of each symbol in the sequence. Similarly, while even 3- and 4-year-olds could produce a graphic symbol sequence following a model, transposing a spoken sentence into a graphic sequence was more difficult for them. Representing an action with graphic symbols was difficult even for 5-year-olds. Finally, the ability to comprehend graphic-symbol sequences preceded the ability to produce them. These developmental patterns, as well as memory-related variables, should be taken into account in choosing intervention strategies with young children who use AAC.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".