Bibliographic record
Abstract
Book Summary: With detailed discussion and invaluable video footage of 23 treatment interventions for speech sound disorders (SSDs) in children, this textbook and DVD set should be part of every speech-language pathologist's professional preparation. Focusing on children with functional or motor-based speech disorders from early childhood through the early elementary period, this textbook gives preservice SLPs critical analyses of a complete spectrum of evidence-based phonological and articulatory interventions. This textbook fully prepares SLPs for practice with a vivid inside look at intervention techniques in action through high-quality DVD clips large and varied collection of intervention approaches with widespread use across ages, severity levels, and populations proven interventions in three categories: direct speech production, broader contexts such as perceptual intervention, and speech movements clear explanations of the evidence behind the approaches so SLPs can evaluate them accurately contributions by well-known experts in SSDs from across the US, Canada, Australia, and the UK An essential core text for pre-service SLPs—and an important professional resource for practicing SLPs, early interventionists, and special educators—this book will help readers make the best intervention decisions for children with speech sound disorders. Evidence-based intervention approaches—demonstrated in DVD clips—such as: minimal pairs perceptual intervention core vocabulary stimulability treatment intervention for developmental dysarthria the psycholinguistic approach
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".