Bibliographic record
Abstract
This study used two methods to assess bilingual dominance in four groups of 18 Italian–English bilinguals, who were selected on the basis of age of arrival (AOA) in Canada (early: 2–13 years; late: 15–26 years) and percentage use of the first language (L1), Italian (low L1 use: 1–15%; high L1 use: 25–85%). Ratios were derived from the bilinguals' self-ratings of ability to speak and understand Italian compared to English (the “verbal” self-rating ratios) and to read and write Italian compared to English (the “written” self-rating ratios). The ratio of the mean duration of English and Italian sentences produced by each bilingual was also computed. AOA and L1 use had the same effect on the self-rating and sentence duration ratios, which were correlated. The bilinguals who arrived in Canada as young adults and continued to use Italian often were the most likely to be Italian dominant. Dominance in Italian was associated with a relatively high level of performance in Italian (assessed in a translation task) and relatively poor performance in English (assessed by measuring strength of foreign accents). Both groups of late bilinguals (late low, late high) and both groups of early bilinguals (early low, early high) were found to produce English sentences with detectable accents. However, a group of 18 bilinguals (all early bilinguals) selected from the original sample of 72 based on their dominance in English did not have detectable foreign accents. This suggested that interlingual interference effects may not be inevitable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".