Instruction and reading samples for opinion writing in L1 junior high school textbooks in China and Japan
Bibliographic record
Abstract
This study examines language arts textbooks commonly used in junior high schools (grades 7, 8, and 9) in Mainland China and Japan in order to identify (1) what kinds of writing instruction are provided; and (2) how reading materials illustrating opinion writing are structured rhetorically. Findings suggest that these textbooks instruct students to follow a direct and linear pattern in opinion writing, represented by such descriptors as “good organization and paragraphing,” “clarity,” “effective supporting details and counter opinions,” and “main point placed at the beginning.” However, unlike prototypical organization of English writing, the statements of main points that appear in the beginning of model texts do not include a preview statement that forecasts the content and organization of the supporting details. This sheds light on culturally situated interpretations of deduction. In addition, a small number of texts exhibit a structure that might be interpreted as quasi-inductive. This interpretation is partly influenced by the difficulty of assigning a single text type to opinion essays. These findings call for further investigation of what purposes these texts serve, how they are written, and whether a gap exists between writing instruction and the actual texts that L1 English student writers are exposed to.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".