Biliterate Adolescents' Writing Skills in a Two-Majority Language Context
Bibliographic record
Abstract
PURPOSE: This study examined the writing skills of English-French bilingual (EFbil) adolescents with extensive exposure to both languages in a two-majority language context. METHOD: = 12) aged 12 to 17 years. All participants wrote image descriptions in French; EFbil also wrote in English. Between-group comparisons were run for complex syntax, errors, and productivity. Within-group comparisons looked at bilinguals across their languages, and oral and written syntax for both groups. Predictors of text quality were explored through multiple regression analyses. RESULTS: In French, both groups performed similarly on productivity and syntactic measures. Bilinguals made more errors, but both groups had a high error-to-productivity ratio. Bilinguals performed similarly in both languages on all measures, except errors that were higher in French. Comparisons between oral and written modalities followed similar patterns for both groups. French exposure and all syntactic measures as well as time spent reading in French were predictive of French text quality. However, a low error-to-productivity ratio best predicted French text quality. CONCLUSIONS: Results indicate a similar syntactic performance in French for monolinguals and bilinguals given comparable French school exposure. Being schooled in French did not prevent bilinguals from developing equivalent writing skills in their first language, English. As a majority and globalized language, the environment appears to have allowed bilinguals to maintain sufficient exposure to support their English skills. These results also point to a mutually contributing relationship between the ability to write good content quality and to respect the spelling and grammar of the language.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".