College English Writing Instruction for Non-English Majors in Mainland China: The “Output-Driven, Input-Enabled” Hypothesis Perspective
Bibliographic record
Abstract
College English writing instruction has been a prominent research area in EFL field in mainland China. This paper has continued the focus by exploring a seemingly effective way for college English writing instruction in China--teaching writing based on reading on the basis of the “output-driven, input-enabled” hypothesis. This hypothesis places emphasis on the important role that language output plays in second language acquisition. Under this hypothesis, language output is both the driving power and objective of EFL teaching; language input provides with the language learners the language forms and content essential for output tasks. This hypothesis meets language learners’ psychological needs, our social needs and current educational needs. In essence, theoretical considerations on carrying out writing instruction based on this hypothesis are discussed. To construct writing instruction, teachers may teach writing based on reading since reading could provide the learners with meaningful language input, which language learners could take advantage of to accomplish the writing tasks. Requirements for writing instructions in reading classes are then identified and illustrations on how to conduct writing are provided under this new hypothesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".