The Effects of Congruent and Systematic Grapheme-Phoneme Correspondences on Novel Word Learning
Bibliographic record
Abstract
Previous studies observed a robust effect of grapheme-phoneme correspondences (GPCs) when learners are exposed to orthographic input while learning novel words. Specifically, if the novel words share the same GPC mapping with learners’ native language, then this Congruency effect helps learning. Likewise, if the GPC is in a one-to-one mapping relation, this effect of Systematicity improves learning too. However, no studies have looked at the interaction of Congruency and Systematicity on word learning or explored both consonant and vowel stimuli. Here, we show no significant orthographic effect when the consonantal GPC mappings were manipulated, particularly because performance was close to the ceiling. And so, while congruent and systematic GPC mappings could facilitate learning, they were not as robust as the literature had suggested. Nevertheless, the effect of Systematicity was significant in the Vowel Sets, likely due to more exposure to the stimuli.
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How this classification was reachedexpand
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".