Acquisition of Variation in the Use of alors, donc, fait que by Advanced French-as-a-Second-Language Learners in Ontario, Canada
Bibliographic record
Abstract
This study examines the acquisition of sociolinguistic variation in the use of French connectors alors/donc/fait que ‘so’ by two groups of advanced French-as-a-second language (FL2) learners in Ontario: (i) high school French Immersion (FI) students and (ii) university students. It considers two types of functions fulfilled by these connectors: (i) the grammatical function of expressing consequence between two clauses and (ii) a range of discursive functions, a dual focus not present in previous research, which considered only one or the other of these two types of functions. Our study shows that: (i) although these two FL2 groups’ use of the connectors is distant from the norms of FL1 speech, the university students achieve a more advanced level of acquisition of this case of variation than do the FI students, reflecting the positive effect of continued learning of French at the postsecondary level; (ii) ‘level of opportunities to interact in French with native speakers’ has a greater positive impact on the acquisition of alors/donc/fait que than ‘time spent learning French’; and (iii) both groups of students evidence incomplete acquisition of the linguistic constraints of connector choice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".