Clinical Implications of Dynamic Systems Theory for Phonological Development
Bibliographic record
Abstract
PURPOSE: To examine treatment outcomes in relation to the complexity of treatment goals for children with speech sound disorders. METHOD: The clinical implications of dynamic systems theory in contrast with learnability theory are discussed, especially in the context of target selection decisions for children with speech sound disorders. Detailed phonological analyses of pre-and posttreatment speech samples are provided for 6 children who received treatment in a previously published randomized controlled trial of contrasting approaches to target selection (Rvachew & Nowak, 2001). Three children received treatment for simple target phonemes that did not introduce any new feature contrasts into the children's phonological systems. Three children received treatment for complex targets that represented feature contrasts that were absent from the children's phonological systems. RESULTS: Children who received treatment for simple targets made more progress toward the acquisition of the target sounds and demonstrated emergence of complex untreated segments and feature contrasts. Children who received treatment for complex targets made little measurable gain in phonological development. CONCLUSIONS: Treatment outcomes will be enhanced if the clinician selects treatment targets at the segmental and prosodic levels of the phonological system in such a way as to stabilize the child's knowledge of subcomponents that form the foundation for the emergence of more complex phoneme contrasts.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".