Using the Canadian Language Benchmarks (CLB) to Benchmark College Programs/Courses and Language Proficiency Tests
Bibliographic record
Abstract
In this article the authors describe a process developed by the Language Training Centre1 (LTC) at Red River College (RRC) to use the Canadian Language Benchmarks (CLB) in analyzing: (a) the language levels used in programs and courses at RRC in order to identify appropriate entry-level language proficiency, and (b) the levels that second language (L2) students need in order to meet college or university entrance requirements based on tests of language proficiency. So far 19 programs and four courses have been benchmarked at RRC. The benchmarking of the programs and courses involved gathering data from various sources at the College and analyzing them by means of CLB descriptors. In addition, a process was developed for using the CLBA and CLB descriptors to benchmark tests: the Canadian Test of English for Scholars and Trainees (CanTEST, 1991) and the Test of English as a Foreign Language (TOEFL). In conclusion, the authors summarize some benefits realized by the benchmarking process. They also address the need to continue to evaluate the results and advise prudent use of the results of these projects.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".