Linguistic Risk-Taking: Exploring the Perspective of Future Language Educators
Bibliographic record
Abstract
This article relates linguistic risk-taking (LRT) to the context of future language educators. LRT refers to language learners stepping beyond their comfort zones by using a target language in challenging communicative situations—experiences that can lead to personal growth, enjoyment and a sense of accomplishment. While LRT has been recognized as a valuable element in language learning, its role among teacher trainees remains underexplored. Yet, this group is uniquely positioned to influence LRT in their future classrooms, acting as practitioners and role models for their future students. Raising awareness among teacher candidates about the educational and emotional benefits of LRT is important. Choosing a participatory approach, a survey of 81 university students training to become English teachers was conducted. The results reveal insights into their perceptions and preferences of LRT, particularily for tailoring the Canadian LRT initiative to their needs (Slavkov & Séror, 2019) within their learning context.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".