Bibliographic record
Abstract
TEM-8 (Test for English Majors-Band 8) is an advanced English proficiency test specifically for English majors in China, and those who pass it are generally considered to have a good command of English. Of this kind of test, proofreading and error correction has long been a headache for teachers and students. The sample analysis in this study reveals that, however, many of the errors in this tricky question type are related to the knowledge of cohesion and coherence, yet few studies have linked the two. Therefore, the present study aims to investigate the distribution of errors in proofreading and error correction in TEM-8 using the cohesion and coherence theory. Errors in authentic TEM-8 test papers of the recent ten years are categorized according to a classification standard which is based on Halliday and Hasan’s theory. The results show that 66% of the errors are related to cohesion and coherence with 56% of the former and 10% of the latter, which is followed by a tentative discussion about each error type from a psycholinguistic perspective. Despite limitations, this study exposes a close association between cohesion and coherence on the one hand, and proofreading and error correction on the other to some extent.
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 | 0.005 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".