Validation of the Results of Linking Speaking Test of IELTS to China’s Standards of English Language Ability
Bibliographic record
Abstract
This study, taking into consideration of teachers’ as well as students’ judgments, explored the generalizability and consistency of the results of linking speaking test of IELTS to China’s Standards of English Language Ability (CSE). Nine college English teachers and 81 undergraduate students judged the degree of congruence between the IELTS speaking test and 72 relevant CSE descriptors to generate evidence of generalizability; 2 teachers judged the CSE levels of 11 videos of IELTS speaking test and 113 students assessed themselves based on the self-assessment scale, to provide data for exploring consistency from three perspectives: consistency of teachers’ judgments, consistency between teachers’ judgments and empirical scores, and consistency between students’ actual ability and the ability demonstrated in the self-assessment scale. The evidence showed that the linking results performed well both in generalizability and consistency from the perspective of both teachers and students, but students rated comparatively low than teachers both in the two facets. However, considering that certain descriptors are relatively abstract and separate from daily life situations in IELTS speaking test, it is understandable that students rate low in generalizability; and considering students' insufficient self-assessment ability, their relative low recognition in consistency also get explained. In general, therefore, the linking results show good generalizability and consistency. 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".