Linguistic Risk-Taking: A Bridge Between the Classroom and the Outside World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it