Reconsideration of the Development of Rating Criteria in Operational Language Testing Contexts
Bibliographic record
Abstract
This paper attempts to explain why the rating criteria for English language oral proficiency tests should be reconsidered and how the perceptions of rating criteria could be applied to language assessment. It introduces the needs of World Englishes (WEs) and their perspectives on the issue of English language testing in Korea, and shows different perceptions of rating criteria for the assessment of English language oral proficiency among three different groups of English teachers: native English teachers in Canada, non-native English teachers in the Philippines, and non-native English teachers in China. This study shows the complex relationships among English teachers’ language backgrounds, perceptions of rating criteria, task types, and their actual rating behaviors. It then focuses on the implications for language assessment of non-native speakers’ English language oral proficiency.
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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 | 0.296 | 0.526 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".