Adapting the Canadian Language Benchmarks for Language Assessment
Bibliographic record
Abstract
The purpose of this article is to describe the development of an instrument for assessing the writing development of students in an English-medium university in Japan. We begin with a description of the setting of the college and the unique nature of its program. Next we discuss the process of selecting a language proficiency framework suitable for the four years of the degree. The Canadian Language Benchmarks (Citizenship and Immigration Canada, 1996) were chosen and subsequently formed the basis for the development of the rating scale. The process of developing the scale held a number of challenges, given the target population and the requirement to have an instrument usable by both language development specialists and nonspecialists. Issues such as the institutional context, the framework for evaluating language development, and development and refinement of the assessment scale over the first two years of the project are discussed.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".