Japanese Students’ Difficulties with Metadiscursive Nouns in Argumentation Essays
Bibliographic record
Abstract
The present study attempts to identify difficulties that Japanese students encounter with metadiscursive nouns in writing second language (L2) argumentation essays. Metadiscursive nouns are abstract and unspecific nouns which can serve as cohesive markers by retrieving their meanings in the text where they occur. Using a selected number of nouns (i.e., problem. reason, thing, fact, idea, decision), this study examines how the nouns, occurring in several syntactic patterns, expressed their meanings in the text and served as metadiscursive devices in L2 essays written by Japanese students, in comparison with essays by American students as a benchmark. The study also discusses the use of problem and reason in relation to rhetorical patterns such as cause-effect clauses and the Problem-Solution text pattern that occurred in the two corpora. A comparison of the ways in which the Japanese and American students use these nouns points to several difficulties the Japanese cohort faces in using metadiscursive nouns in argumentative essays: providing a focus in describing information, making an explicit meaning link, and using particular English rhetorical patterns. Suggestions are made for further inquiries which could broaden our understanding of the behavior of this class of nouns and inform the teaching of L2 argumentation essays.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".