Aligning Language Frameworks: An Example with the CLB and CEFR
Bibliographic record
Abstract
This paper presents a methodology for directly aligning ‘can do’ frameworks to each other. The methodology, inspired by the manual for relating examinations to the Common European Framework of Reference for Languages: Learning, teaching, assessment (CEFR) (Council of Europe, 2009) and Kane’s (2004, 2013) interpretative argument, takes account of both the horizontal dimension (content analysis) and the vertical dimension (benchmarking with Multifaceted Rasch Modelling – MFRM). The paper exemplifies the application of the methodology by introducing the research conducted to align the Canadian Language Benchmarks (CLB)/ Niveaux de compétence linguistique canadiens (NCLC) to the CEFR, presenting the resulting alignment, and discussing the rationale for the choices made.
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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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".