A Corpus-based Re-categorization for English and Chinese Hedges
Bibliographic record
Abstract
Hedge is an interesting linguistic phenomenon that plays a crucial role in cross-cultural communication. Exploring the differences in hedge use between Chinese and English discourse, as well as the reasons behind them, can further reveal the ideological significance reflected in the use of hedges and their role in the construction of discursive power. Based on two self-built corpora, comprising 122 speeches and statements from the Chinese and Malaysian Ministry of Foreign Affairs between the years 2023 and 2024, the study aims to establish a detailed and unified re-categorization for both English and Chinese hedges to enhance the operationalization of corpus linguistics for retrieving the usage characteristics of different types of hedges in Chinese and English discourse, thereby facilitating a cross-linguistic contrastive analysis. Based on Prince et al.’s (1982) and He’s (1985) categorization, as well as Varttala’s (2001) categorization of hedges from a cognitive lexical perspective, this study divides the existing four types of hedges – adaptors, rounders, plausibility shields, and attribution shields – into 10 specific sub-types based on their pragmatic functions. The finding indicates that, among the ten subcategories of hedges, the usage differences in eight subcategories between the Chinese and Malaysian corpora show significant differences. Therefore, the new subcategories of hedges presented in this article provide valuable insights for future scholars to conduct statistical analysis using corpus linguistics in contrast analysis between English and Chinese discourse.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".