A Made-in-Canada Second Language Framework for K-12 Education: Another Case Where No Prophet is Accepted in their Own Land
Bibliographic record
Abstract
To ensure quality of education, a language framework should be the foundation on which second language curricula are developed. In 2010, the Council of Ministers of Education, Canada (CMEC), as suggested by Vandergrift (2006a, 2006b), recommended the use of the Common European Framework of Reference (CEFR) in the K-12 Canadian school context and presented several considerations for adaptation before it should be adopted and used. Although the CEFR is partially used across Canada, few of the CMEC’s considerations have been met to date. Given this state of affairs, we suggest the made-in-Canada, Canadian Language Benchmarks and les Niveaux de compétence linguistique canadiens (CLB/NCLC) as an alternative. We argue that the CLB/NCLC, profoundly revised in 2012, best embrace the Canadian context and, using Vandergrift’s criteria for a valid language framework, that CLB/NCLC are now superior to the CEFR in many aspects.
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.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".