Category restructuring during second-language speech acquisition
Bibliographic record
Abstract
This study examined the production of English /b/ and the perception of short-lag English /b d g/ tokens by four groups of bilinguals who differed according to their age of arrival (AOA) in Canada from Italy and amount of self-reported native language (L1) use. A clear difference emerged between early bilinguals (mean AOA= 8 years) and late bilinguals (mean AOA= 20 years). The late bilinguals showed a stronger L1 influence than the early bilinguals did on both the production and perception of English stops. In experiment 2, the late bilinguals produced a larger percentage of prevoiced English /b/ tokens than early bilinguals and native English (NE) speakers did. In experiment 3, the late bilinguals misidentified short-lag English /b d g/ tokens as /p t k/ more often than the early bilinguals and NE speakers did. Experiment 4 revealed that the frequencies with which the bilinguals prevoiced /b d g/ in Italian and English were correlated. The observed differences between the early and late bilinguals were attributed to differences in the quantity and quality of English phonetic input they had received, not to a greater likelihood by the early than late bilinguals to establish new phonetic categories for English /b d g/.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".