A Corpus-Based Approach to Investigate the Cohesive Features Across Different Levels of CEFR
Bibliographic record
Abstract
Despite plenty of previous studies pointing out the importance of validating the CEFR scale, scant attention has been given to the validation of the CEFR cohesion scale based on learners’ corpus. This study aims to examine the cohesive features of written texts at different levels of the CEFR using a corpus-based approach. Employing the TAACO and Coh-Metrix tools, this study identified identifies seven categories of key cohesive features, namely connectives, lexical overlap (sentence), Type-token ratio (TTR) and Density, givenness, semantic overlap, hypernymy and deep cohesion of the CEFR. The results showed that hypernymy and deep cohesion were the strongest predictors to distinguish CEFR levels and these categories generally kept a nonlinear relationship with CEFR levels. This study provides empirical evidence to further validate and refine the CEFR cohesion scale and casts light on the development of cohesive competence across different levels of the CEFR from the perspective of second language acquisition. More importantly, this study can provide pedagogical implications for learning and assessing cohesive competence.
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 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 | 0.032 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".