Indicators of an “Immigrant Advantage” in the Writing of L3 French Learners
Bibliographic record
Abstract
Grounded in the cross-linguistic influence(s) (CLI) literature, this study used objective measures to compare the use of English, lexical richness and syntactic complexity, and grammatical accuracy and fluency in the texts of three groups of Grade 6 French immersion students: Canadian-born anglophones (C-A), Canadian-born multilinguals (C-M), and immigrant multilinguals (I-M). Findings identified use of English, vocabulary richness, and grammatical accuracy as the discriminating variables that had the most effect on the quality of writing of the three groups. The differences in performance on these specific writing aspects were most salient between the C-A and I-M groups. We propose that, in our data, the social status of immigrants might have a more profound influence on a student’s approach to language learning and investment than that of being multilingual, and that this can translate directly in certain areas of language performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".