Linguistic Risk-Taking: A Bridge Between the Classroom and the Outside World
Bibliographic record
Abstract
This article describes an initiative launched at a Canadian bilingual university in order to encourage L2 French and L2 English learners to take ‘linguistic risks’: authentic, autonomous communicative acts where learners are pushed out of their linguistic comfort zone. The initiative was operationalized through the development of a Linguistic Risk-Taking Passport, which contains 74 linguistic risks that students can take in their L2 across the university campus and in their everyday life. An analysis of interviews with participating teachers (n=6) and learner self-report data from completed passports (n=410) examines how the initiative was integrated into the classroom and which passport items were perceived by students as particularly high-risk. A cyclical process of risk-taking within a broad Task-Based Language Teaching (TBLT) framework is described in which risks are viewed as learner-selected tasks with a dynamic affective slant; risks can be used to connect classroom learning with real-life L2 use and vice versa. The data illustrate that linguistic risk-taking can help TBLT practitioners generate ideas on how to narrow the gap between the classroom and the real-world. The article concludes with a list of practical implications and suggestions for adapting linguistic risk-taking to other institutional contexts.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.036 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".