Testing the statistical learning of spelling patterns by manipulating semantic and orthographic frequency
Bibliographic record
Abstract
ABSTRACT This study tested the diverging predictions of recent theories of children's learning of spelling regularities. We asked younger (Grades 1 and 2) and older (Grades 3 and 4) elementary school–aged children to choose the correct endings for words that varied in their morphological structure. We tested the impacts of semantic frequency by including three types of words ending in -er: derived and inflected forms, the first of which are far more frequent across the language, and one-morpheme control forms. Both younger and older children were more likely to choose the correct ending for derived forms over one-morpheme control words. This difference emerged for inflected forms only for the older children. We also tested the impacts of orthographic frequency by contrasting performance on the two derived allomorphs -er and -or, the first of which is far more frequent, and comparison one-morpheme forms. Both younger and older children were more likely to choose the correct spelling for the derivational -er over the same letter pattern in control words. This difference did not emerge in either group for the -or ending. The implications of these findings for current models of children's spelling development are discussed.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".