The Common European Framework of Reference (CEFR) in Canada: A Research Agenda
Bibliographic record
Abstract
This article proposes a research agenda for future inquiry into the use of the Common European Framework of Reference (CEFR) in the plurilingual Canadian context. Drawing on data collected from a research forum hosted by the Canadian Association of Second Language Teachers in 2014, as well as a detailed analysis of Canadian empirical studies and practice-based projects to date, the authors examine three areas of emphasis related to CEFR use: (a) K-12 education, including uses with learners; (b) initial teacher education, where additional language teacher candidates are situated as both learners and future teachers; and (c) postsecondary language learning contexts. Future research directions are proposed in consideration of how policymaking, language teaching and language learning are articulated across each of these three contexts. To conclude, a call is made for ongoing conversations encouraging stakeholders to consider how they might take up pan-Canadian interests when introducing various aspects of the CEFR and its related tools.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.029 | 0.030 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".