A Multidimensional Development Study of Written English Complexity of High-level Non-English Major Students
Bibliographic record
Abstract
Writing is widely regarded as one of the most important parts in the field of second language learning and teaching. The main assessment elements of L2 writing can be divided into accuracy, fluency and complexity. The evaluation of writing, accuracy, fluency and complexity can not only measure the writing achievement of L2 writers, but also reflect their writing ability. Based on the complexity of writing, this study adopts the dynamic system theory to investigate the multidimensional development of written English complexity. Three sophomores of high-level non-English majors in a university are selected as subjects to track the development and interaction patterns of eight linguistic complexity indices in 21 essays over three semesters at three levels: lexical construction, sentence construction and phrase construction. The results show that the change and development of subjects in this respect are not linear, but there are peaks, troughs, progress and regression. The development of written language has obvious dynamic and variability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".