Human Ratings of Writing Quality Capture Features of Syntactic Variety and Transformation in Chinese EFL Learners’ Argumentative Writing
Bibliographic record
Abstract
Traditionally, writing quality is measured by human ratings, either holistically or analytically. The present study aimed to investigate the locus of human ratings by analyzing the linguistic features that are predictive of writing quality. One hundred and 44 argumentative writing samples from Chinese learners of English as a foreign language were evaluated by human ratings and quantitative measurement of writing quality indexed by Coh-Metrix. Holistic and analytic human ratings had significant correlations with quantitative measures related to syntactic variety and transformation. Moreover, linear and logistic regressions revealed that syntactic simplicity, words before main verb, syntactic structure similarity in all sentences and across paragraphs, incidence of passive voice and temporal connectives were five valid indices that can consistently differentiate writing quality indexed by human ratings. The present findings have significant pedagogical implications for human ratings on writing quality in the foreign language learning context.
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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 | 0.000 |
| 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) | 0.001 | 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".