Do the Hard Things First: A Randomized Controlled Trial Testing the Effects of Exemplar Selection on Generalization Following Therapy for Grammatical Morphology
Bibliographic record
Abstract
Purpose: Complexity-based approaches to treatment have been gaining popularity in domains such as phonology and aphasia but have not yet been tested in child morphological acquisition. In this study, we examined whether beginning treatment with easier-to-inflect (easy first) or harder-to-inflect (hard first) verbs led to greater progress in the production of regular past-tense -ed by children with developmental language disorder. Method: Eighteen children with developmental language disorder (ages 4-10) participated in a randomized controlled trial (easy first, N = 10, hard first, N = 8). Verbs were selected on the basis of frequency, phonological complexity, and telicity (i.e., the completedness of the event). Progress was measured by the duration of therapy, number of verb lists trained to criterion, and pre/post gains in accuracy for trained and untrained verbs on structured probes. Results: The hard-first group made greater gains in accuracy on both trained and untrained verbs but did not have fewer therapy visits or train to criterion on more verb lists than the easy-first group. Treatment fidelity, average recasts per session, and verbs learned did not differ across conditions. Conclusion: When targeting grammatical morphemes, it may be most efficient for clinicians to select harder rather than easier exemplars of the target.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".