Performance of Children with Typical Development When Reading and Interpreting Graphic-Symbol Sequences<sup>*</sup>
Bibliographic record
Abstract
In order to understand a sequence of graphic symbols as sentences, one must not only recognize the meaning of individual symbols but also integrate their meaning together. In this study children without disabilities were asked to perform two tasks that presented sequences of graphics as stimuli but that differed in the need to treat the symbols as a sentence (i.e., with evidence of relationships among the individual symbols): a "reading" task (transpose the symbol sequence into speech), and an act-out task (demonstrate the meaning of the symbol sequences using puppets). The participants, aged 3 (n=18), 4 (n=36), 5 (n=27), and 6 (n=23) years, all succeeded on the reading task, but the younger groups were much less successful than the older groups on the act-out task. The children were more likely to pass the act-out task if they used conjugated rather than infinitive verb forms in their spoken responses on the reading task. In the younger age groups, children who used conjugated verb forms had higher receptive vocabulary scores. The findings suggest that being able to reproduce a sequence of symbols does not guarantee that the symbols are treated as a sentence. The inclusion in the study of children who were able to respond using speech, permitted observation of two types of responses (conjugated versus infinitive verb forms) that revealed different levels of understanding of graphic symbol sequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".