Understanding semantic accents in Japanese–English bilinguals: A feature-based approach
Bibliographic record
Abstract
Abstract A bilingual exhibits a “semantic accent” when they comprehend or use a word in one language in a way that is influenced by its translation. Semantic accents are well-captured by feature-based models: however, few studies have specifically examined the processing of features that contribute to a semantic accent. Japanese–English bilinguals and monolinguals of each language completed three feature-based tasks focusing on culture-specific semantic features. Bilinguals exhibited semantic accents in L1 and L2 in that they had stronger associations than monolinguals between the features specific to one culture and words in the other language. Within bilinguals, culture-specific features were more strongly associated with the congruent language than the incongruent language. Finally, changes in the strengths of associations between culture-specific features and words depended more on L2 cultural immersion than L2 proficiency. Semantic accents lessened in L2 and increased in L1 after many years of exposure to the L2 culture.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".