The influence of classroom input and community exposure on the learning of variable grammar
Bibliographic record
Abstract
As pointed out by Carroll (Carroll), our team has investigated the influence of input on the spoken French competence of older Ontario bilinguals. Our research has examined the learning of invariant and variable aspects of French grammar. We focus here on the learning of variation, since it is an under-researched topic not covered by Carroll. Our research examines adolescent speakers of Ontario French from French-medium schools (e.g., Mougeon & Beniak, 1991), same-age immersion students (e.g., Mougeon, Nadasdi & Rehner, 2010) and advanced learners from a bilingual university (e.g., Mougeon & Rehner, 2015). Two key dimensions of input are teacher classroom speech and frequency of use of French in the community for the Franco-Ontarian students and amount of extra-curricular interactions with Francophones for the FSL students. Having collected corpora from these student groups, we compared the output of learners with primarily classroom-based input with that of learners with broader ranging (extra-) curricular input. The availability of teacher in-class recordings for these learner groups has been crucial in identifying additional factors influencing these students’ output.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".