The functional phonological unit of <scp>J</scp>apanese‐<scp>E</scp>nglish bilinguals is language dependent: Evidence from masked onset and mora priming effects
Bibliographic record
Abstract
Abstract Speech production research has shown that J apanese monolingual speakers use mora‐sized phonological units, not phoneme‐sized units, when phonologically encoding J apanese words. Recent bilingual research has indicated that proficient J apanese‐ E nglish bilinguals nevertheless use phoneme‐sized units when phonologically encoding E nglish words, suggesting that use of a phonological unit that is smaller than that of their L 1 develops with increasing proficiency in E nglish. The purpose of the present research was to determine whether proficient J apanese‐ E nglish bilinguals also begin to use the smaller, phoneme‐sized units when producing J apanese words. In a masked priming naming task, proficient J apanese‐ E nglish bilinguals produced a significant masked onset priming effect for E nglish words, confirming that they do use phoneme‐sized units when phonologically encoding in E nglish ( L 2). These bilinguals, however, showed only mora‐based facilitation for J apanese words in an experiment involving only J apanese words. These results suggest that proficient bilinguals use different unit sizes depending on the language being produced, and that for bilinguals whose L 1 and L 2 have different unit sizes, the phonological encoding process is at least somewhat different in their two languages.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".