What methods of scoring young children's spelling best predict later spelling performance?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Children's spellings are often scored as correct or incorrect, but other measures may be better predictors of later spelling performance. METHOD: We examined seven measures of spelling in Reception Year and Year 1 (5-6 years old) as predictors of performance on a standardized spelling test in Year 2 (age 7). RESULTS: Correctness was the best predictor of later spelling by the middle of Year 1, and it significantly outperformed a binary measure of phonological plausibility at the end of Reception Year. Nonbinary measures based on Levenshtein distance were significant predictors of later spelling in the middle of Reception Year and in children who produced no correct spellings. Some widely used scales performed less well with children who did not yet produce any correct spellings. CONCLUSIONS: Nonbinary measures of spelling performance can predict later spelling performance, but for a more restricted period than anticipated based on many theories.
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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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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