Pragmatic Rating of L2 Refusal: Criteria of Native and Non-native English Teachers
Bibliographic record
Abstract
Many studies have shed light on rater criteria for assessing the performance of language skills (e.g., Eckes, 2005). However, the interface between rater assess- ment and interlanguage pragmatics (ILP) has remained largely unnoticed. To address this interface, this study explored the ratings native (NES) and nonnative English speaking (NNES) teachers assigned to second language (L2) refusal pro- duction and the criteria they applied in their ratings. To this end, 50 NES and 50 NNES teachers participated in rating L2 refusal production of EFL learners that included responses to a 6-item written discourse completion task. The data were analyzed qualitatively and quantitatively. Qualitative analysis showed that na- tive teachers applied 11 criteria and nonnative teachers applied 6 criteria in their pragmatic ratings. Reasoning/explanation was the leading criterion in teacher assessment among native raters, whereas politeness was the main criterion for nonnative ratings. Quantitative analysis documented variation in the frequency of drawing on rating criteria and significant differences in ratings, with NNES teachers being more lenient and divergent in their ratings. The results suggest there is a gap between NES and NNES teachers in terms of rating criteria, strictness, and convergence in rating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".