Bibliographic record
Abstract
Two current developments reflecting a common concern in second/foreign language assessment are the development of: (1) scales for describing language proficiency/ability/performance; and (2) criterion-referenced performance assessments. Both developments are motivated by a perceived need to achieve communicatively transparent test results anchored in observable behaviors. Each of these developments in one way or another is an attempt to recognize the complexity of language in use, the complexity of assessing language ability, and the difficulty in interpreting potential interactions of scale task, trait, text, and ability. They reflect a current appetite for language assessment anchored in the world of functions and events, but also must address how the worlds of functions and events contain non skill-specific and discretely hierarchical variability. As examples of current tests that attempt to use performance criteria, the chapter reviews the Canadian Language Benchmark, the Common European Framework, and the Assessment of Language Performance 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 | 0.092 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".