Learning Accurate Onset Clusters: Perception Lags Behind Production
Bibliographic record
Abstract
This study investigates young school-aged children's knowledge (at 4-7 years) of accurate English word-initial onset clusters. By this age, we expect children to be mostly accurate in producing #CC clusters (rather than repairing them with deletion or epenthesis). We ask how well can they recognize and reject cluster repair errors, in both real and nonce word tasks. The results suggest that these learners' cluster judgment skills lag behind their cluster production abilities, and that asymmetries in error types do not overall align between the two domains. Perceptual errors are made most often when comparing clusters with epenthesis repairs, not deletion, and the cluster's sonority profile does not directly influence error rates. After comparing these findings with similar results from adult L2 English speakers as well, we discuss the ways in which issues like recoverability, salience, and contiguity can account for our findings. We also suggest that more work on phonological knowledge and judgments in older children will provide a broader understanding of sound pattern acquisition across 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".