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.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".