The Effect of Educational Input on the Development of Sociolinguistic Competence by French Immersion Students: The Case of Expressions of Consequence in Spoken French
Bibliographic record
Abstract
In this paper we discuss the results of a comparative study examining the words used by 41 Grade 9 and 12 OntarioFrench immersion students to express the notion of consequence intersententially (i.e., between two clauses). The immersionstudents' usage is compared to that found in: a) the spoken Frenchof Quebeckers, b) the in-class speech of a sample of French immersion teachers, and c) a series of French language artsteaching materials used in French immersion programs.The comparison reveals that this notion of consequence is expressed bytwo variants, namely alors and done, that are used with differential frequencies in all four corpora, another variant, namely (9a) faitque, that is used almost exclusively by the speakers of Quebec French and a final variant, namely so, that is used only by the immersion students. Explanations for these findings are tied to a number of factors (e.g., the differential sociostylistic values attached to these variants, and inter- and intra-systemic properties of the variants). For instance, the absence of the frequent variant(9a) fait que in the student and educational corpora reflects this variant's vernacular status in Quebec French and, hence, its avoidance in the students' educational input. Also, the students' useof so is attributed to their not having fully automatized the French conjunctions of consequence and their occasional switching to thisEnglish equivalent. Finally, we discuss the pedagogical implications of our findings.
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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 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".