A Comparative Study on Engagement Resources in American and Chinese CSR Reports
Bibliographic record
Abstract
Based on Martin and White’s (2005) heteroglossic engagement system of Appraisal Theory, adopting UAM Corpus Tool and Chi-Square test, this study aims to explore authorial stance and distinctive rhetorical strategies that have been employed to realize interpersonal meaning by the application of engagement resources in American and Chinese CSR reports. It can be concluded that all types of engagement resources are widely employed in both American and Chinese corpus, with contraction resources significantly different in two corpora. It also finds that American CSR reports employ each type of engagement markers equally, while Chinese CSR reports tend to highly use expansion resources to enhance authorial voice. Besides, American CSR reports employ contraction resources in a more diversified and flexible way than Chinese CSR reports writers do. Lastly, they are also different in acknowledging what kinds of external propositions as expansion resources. This study confirms that engagement system is an important tool to help CSR reports writers to align authorial voices with readers, thereby accomplishing promotional and persuasive purposes. It may give some implications to CSR report addressers, addressees, business English teaching and reading.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".