An Empirical Study of the Effects of Output and Model Composition Input on Second Language Writing
Bibliographic record
Abstract
Currently, English teaching in China lays excess emphasis on language input but ignores the function of language output. This study aims to conduct an empirical study on the effects of output and model composition input on second language writing. Three problems are going to be explored: (a) The language features noticed by students in the process of model composition study; (b) The validity of the effects of output and model composition study on second language writing from the perspectives of influence, accuracy, and complexity; (c) differences in the noticing process and learning outcomes among different levels of learners. Statistics show that in reading the model composition vocabulary is the first thing that is noticed, not grammar, content, rhetoric or discourse structure. Output and model composition study play an important role in enhancing the accuracy of writing. The learning outcomes vary among different levels of language learners. It is discovered that the output-driven teaching model is conducive to the reform of the traditional teaching concept and model compositions by native speakers could be regarded as an effective way of teacher feedback in second language writing.
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".