Comments from the Chalkface Margins: Teachers’ Experiences with a Language Standard, Canadian Language Benchmarks
Bibliographic record
Abstract
While the Canadian Language Benchmarks (CLB) document has been a milestone in supporting the teaching of English as an additional language to adults in Canada, few studies examined practitioners’ experiences with the language standard. The expectation of ongoing use of the CLB by teachers in the Language Instruction for Newcomers to Canada (LINC) program became a rigid requirement with the implementation of portfolio-based language assessment (PBLA). However, the CLB-related literature has been mostly conceptual and aspirational, while practitioners’ voices have been on the margins of research and policy making. This article examines teacher comments on the CLB, as collected during a large mixed-methods exploratory project on PBLA implementation (Desyatova, 2018, 2020). While some practitioners appreciated the standard and its impact, the majority of comments reflected comprehensibility and interpretation challenges, experienced by both teachers and learners. These challenges were further aggravated by the pressures of PBLA as a mandatory assessment protocol.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".