Learning Accurate Onset Clusters: Perception Lags Behind Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it