On the Learning of Auxiliary Use in the Referential Variety by Speakers of New Brunswick Acadian French
Bibliographic record
Abstract
This study investigates the learning of Referential French by speakers of Acadian French at the university level. One difference between the two varieties lies in their use of auxiliaries in compound tenses. In Acadian French, avoir is used categorically in compound tenses with verbs of inherently directed motion and pronominal verbs, while Referential French uses être. A controlled-production task and an acceptability judgment task were administered to 80 speakers of New Brunswick Acadian French who were students at a francophone university in New Brunswick, 40 first-year students and 40 fourth-year students. Results show that, while there is still variability in the fourth-year students’ auxiliary use, their performance is significantly closer to Referential French than that of the first-year students. Cet étude examine l’apprentissage du français de référence pas les locuteurs du français acadien au niveau universitaire. Les deux variétés se distinguent par leur utilisation des auxilairires dans les temps composés. En français acadien, on utilise avoir catégoriquement dans les temps composés avec les verbes de motion intrinsèquement dirigés et les verbes pronominaux, tandis que le français de référence utilise être. On a administré un test lacunaire et des jugements de l’acceptabilité à 80 locuteurs de français acadien du Nouveau-Brunswick qui étaient des étudiants à une université francophone dans la province, 40 étudiants dans leur première année d’études et 40 dans leur quatrième année. Les résultats indiquent que, bien qu’il y a toujours de la variabilité dans l’emploi des auxiliaires chez les étudiants en quatrième année, leur performance est plus proche au français de référence que celle des étudiants en 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".