Communicative acts of a child with Rubinstein—Taybi syndrome during early communicative development
Bibliographic record
Abstract
Rubinstein—Taybi syndrome (RTS) is a rare genetic developmental disorder that often shows associated language delay. However, literature on language development in RTS is very limited, particularly for the period of early communicative development, when standardized testing can be minimally informative. The purpose of the current study was to present a descriptive profile of the intentional communicative acts of a 4-year-old child with RTS, both to add to the literature on the syndrome, and as an example of non-standard profiling of early communication. The child was video-recorded in a natural context with familiar conversational partners. Analyses of the videotaped interactions included rate of communication, communicative functions and modes, and discourse patterns in terms of initiation and response. The child's mean rate of communication was 6 communicative acts per minute. In terms of communicative function, assertive (commenting) communicative acts were most common, followed by directive (requests) and expressive (affective) acts. Mode of communication was less advanced than communicative function; vocalizations were the most frequent mode, although linguistic skills were emerging (signs, single words). Implications for treatment are suggested for children with development delays on the threshold between prelinguistic and one-word phases of development.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".