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Record W3192813533 · doi:10.1111/flan.12561

Linguistic risk‐taking in second language learning: The case of French at a Canadian bilingual institution

2021· article· en· W3192813533 on OpenAlex
Martine Rhéaume, Nikolay Slavkov, Jérémie Séror

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueForeign Language Annals · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCanadian Virtual UniversityUniversity of Ottawa
Fundersnot available
KeywordsInstitutionPsychologyConstruct (python library)PedagogyParticipatory action researchBilingual educationLinguisticsLanguage assessmentMetacognitionSociologyComputer scienceCognition

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.034
GPT teacher head0.288
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it