Exploring the Rhetorical Use of Interactional Metadiscourse: A Comparison of Letters to Shareholders of American and Chinese Financial Companies
Bibliographic record
Abstract
By taking Hyland and Tse’s (2004) interactional metadiscourse model, this study attempts to compare the incidence of the interactional metadiscourse markers in letters to shareholders of American and Chinese companies in the financial industry and their rhetorical functions. This study makes American corpus of 41 letters and Chinese corpus of 37 letters both of which are written in English. WordSmith is adopted to find out the differences in incidence of these markers. Chi-square test further confirms these differences are significant. It is consequently found that five types of interactional metadiscourse markers are all deployed in both American and Chinese corpus but the incidence of each marker is much more in American corpus than that in Chinese corpus. Besides, this study identifies that self-mentions and engagement markers are the most frequently employed in both corpora. However, it is noticeable that self-mentions and engagement markers collaborate with another three types of markers more tactically and flexibly in American corpus than in Chinese. In particular, self-mentions markers are found to use with not only boosters, but hedges and engagement markers in American corpus, while self-mentions markers in Chinese corpus only collaborate with hedges. In addition, the engagement markers of model verbs and personal pronouns are more active in American corpus than these in Chinese. This study finds out American companies integrate these markers into building a positive personal or corporate image and closing writer-reader relationship, thereby accomplishing the promotional and persuasive purposes. It may suggest that Chinese companies employ the integration of interactional metadiscourse markers as strategically as American.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".