Comparing Written Competency in Core French and French Immersion Graduates
Bibliographic record
Abstract
Abstract Few studies have compared the written competency of French immersion students and their core French peers, and research on these learners at a postsecondary level is even scarcer. My corpus consists of writing samples from 255 students from both backgrounds beginning a university course in French language. The writing proficiency of core French and French immersion graduates was compared based on total output and several measures of grammatical and syntactical accuracy. Few statistically significant differences emerge. However, a subgroup of core French learners who had benefitted from an authentic immersion experience appears to outperform both regular core French and French immersion groups. The purpose of this quantitative study is primarily diagnostic; the results should help universities better serve the needs of first-year students. Résumé Les études comparant la compétence écrite des étudiants de programmes d’immersion française et de français cadre sont peu nombreuses—particulièrement au niveau postsecondaire. Mon corpus consiste en des échantillons du français écrit de 255 étudiants issus de ces deux formations qui commencent un cours de français à l’université. J’ai comparé leur production globale et leur précision sur le plan morphosyntaxique. Peu de différences statistiquement significatives en émergent. Toutefois, un sous-groupe d’étudiants cadre ayant bénéficié d’une expérience d’immersion authentique se révèle comme le plus compétent selon plusieurs des mesures utilisées. Les résultats de cette étude quantitative devraient aider les universités à mieux répondre aux besoins des étudiants de première année.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".