Developing crosslinguistic awareness through plurilingual consciousness-raising tasks
Bibliographic record
Abstract
If maximal exposure were the key to success in language learning, then adult learners at the university level would be doomed to fail. Not only are they presumably too old to learn additional languages effectively, but target language (TL) input appears to be insufficient, especially when other languages are allowed in class. Nevertheless, learners were shown to build on knowledge of previously acquired languages, to rely on language learning experience and to develop metalinguistic awareness. This study explores the perceived usefulness of a plurilingual consciousness-raising task that aims at helping learners make and strengthen connections between the TL and other previously acquired languages. Two university-level language courses were targeted: Spanish in Quebec and French in Mexico. Two customized tasks were implemented and recorded in each course throughout the semester. Each task included an input-based (discovery) phase, a reflective (metalinguistic) phase during which participants were asked to make assumptions on underlying patterns and correspondences across languages, and a validation phase where they presented their assumptions until reaching a consensus as a group. While tasks were generally perceived as useful, analyses of post-task questionnaires also revealed mixed feelings regarding its inductive stance. However, the verbal data collected demonstrated that the collaborative and metalinguistic reflective nature of the task permitted learners to find correspondences between languages and to engage in knowledge construction. Moreover, the various reflections collected indicate that learners benefitted from the task as groups engaged in metalinguistic reflections, activated their plurilingual repertoire and were able to create accurate assumptions regarding the targeted structure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".