Linguistic risk‐taking in second language learning: The case of French at a Canadian bilingual institution
Bibliographic record
Abstract
Abstract This article focuses on the construct of linguistic risk‐taking and outlines a new pedagogical initiative implemented at a Canadian bilingual postsecondary institution. The Linguistic Risk‐Taking Initiative aims at encouraging language learners to target specific challenges and seek opportunities to practice their second official language (French or English) in authentic contexts beyond the language learning classroom. Using the lens of participatory action research, the article reports on how a Linguistic Risk‐Taking Passport is used to support language learners’ autonomous language practice in combination with metacognitive awareness activities and goal setting. A teacher's reflections and surveys with 296 student participants over five semesters indicate that linguistic risk‐taking offers promise both in terms of innovative and engaging pedagogical practices and in terms of language teaching research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.053 | 0.012 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".