Language as a vehicle or as a resource? Exploring the nature of metalinguistic reflection in plurilingual consciousness-raising tasks
Bibliographic record
Abstract
While studies have shown that additional language (Lx) learners build on knowledge of previously acquired languages (Ringbom 2007), the natural interaction between languages is rarely exploited in Lx classrooms. This study explores the nature of metalinguistic reflections and crosslinguistic connections during plurilingual consciousness-raising tasks (PluriL-CRT). Three collaborative PluriL-CRTs targeting specific target language (TL) structures were implemented and recorded in a higher education German Lx classroom in Quebec, Canada. Discussions were analyzed for metalinguistic reflections with or without crosslinguistic connections and for levels of analysis (superficial vs. complex) in terms of Form – Meaning – Use (Larsen-Freeman 2014). Analyses suggest that when crosslinguistic connections are made, learners engage in qualitatively different levels of analysis depending on the given TL structures and specific language combinations. The framework of analysis detailed in this study is meant to serve future research into the effects of plurilingual classroom practice that involves metalinguistic reflection on Lx development.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".