Different Genres and Proficiency Levels: Relationships Among Syntactic Complexity, Genres and Students with Different L2 Proficiency Levels
Bibliographic record
Abstract
The present study examines differences in syntactic complexity in English writing among writers at different levels and explores the relationship between syntactic complexity and writings with different genres. 20 students in grade three of a senior high school that were randomly selected from two brands of test scores were grouped into high and low proficiency groups. The 40 writings from the 20 students were examined. Writings were evaluated by L2SCA (L2 Syntactic Complexity Analyzer) gauging syntactic complexity at global, clausal and phrasal level. After obtaining the data, the complexity values were entered into SPSS 21.0 to do analysis. Results reveal that the difference of the two genres reaches a significant level in terms of C/T (clauses per T-unit) and CN/C (complex nominals per clause); there is no significant relationship between syntactic complexity and L2 proficiency levels and no significant interactive effect is found between the genre factor and proficiency factor. The results can yield implications for ESL writing pedagogy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".