Can beginner L2 learners handle explicit instruction about language variation? A proof-of-concept study of French negation
Bibliographic record
Abstract
Research has pointed to the importance of introducing social aspects of language at the beginning stages of second language (L2) acquisition (Yates, 2017 Yates, L. (2017). Learning how to speak: Pronunciation, pragmatics and practicalities in the classroom and beyond. Language Teaching, 50(2), 227–246. https://doi.org/10.1017/S0261444814000238[Crossref], [Web of Science ®] , [Google Scholar]). This proof-of-concept study therefore sought to determine if an explicit pedagogical intervention consisting of various types of sociolinguistic awareness activities could be implemented with beginner learners of French to bring about changes in knowledge about form, meaning and use of French negation. A beginner university-level French course (N = 22) received systematic explicit instruction on language variation over a 15-week period, targeting the variable use of the negative morpheme ne in verbal negation in French. To assess the effects of instruction on declarative knowledge, participants provided L1 explanations about the target feature at the beginning (Time 1) and end of the course (Time 2).They also displayed application of the rule in writing at Time 1 and 2. Findings point to increased awareness of variable presence of ne and its use, as well as increased ability to use target features in their appropriate contexts of use, suggesting that introduction of sociolinguistic features at early stages of acquisition can benefit L2 learners without confusing or overwhelming them. Discussed are the potential benefits of implementing pedagogical strategies to increase beginner learners’ sociolinguistic awareness.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".