Lexical and Rhetorical Patterns of Scientific Discourse in Ginseng Studies
Bibliographic record
Abstract
This study takes English-language scientific literature in the field of ginseng research as its corpus to explore the lexical features and rhetorical structures of scientific discourse.By integrating Systemic Functional Linguistics and Swales' genre analysis theory, and employing corpus-based methods, the research identifies high-frequency terminology, semantic collocations, and rhetorical strategies-such as the extensive use of ginsenoside-related terms, evaluative vocabulary, and frequent hedging expressions.The findings reveal that ginseng research articles commonly follow the IMRaD structure, with rigorous argumentation and clearly defined rhetorical moves.Moreover, strategies such as self-mention and citation are widely used in constructing academic identity.Cross-disciplinary and cross-cultural linguistic variations are also evident, particularly in non-native English writing, where stylistic transfer and register adjustment frequently occur.This study aims to fill the gap in discourse analysis within ginseng research, enrich the theoretical and practical dimensions of academic linguistics, and provide empirical references for non-native English writers composing international scientific articles, thereby promoting the global dissemination and academic exchange of traditional Chinese medicine.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".