A Discourse Analysis of CSR Reports for Chinese and American Pharmaceutical Enterprises Based on Fairclough’s Three-dimensional Model
Bibliographic record
Abstract
Based on Fairclough’s three-dimensional modelling framework, this paper presents a discourse analysis of the corporate social responsibility (CSR) reports of AbbVie and Hengrui, representative companies in the pharmaceutical field in the United States and China. At the textual analysis level, word frequency statistics are conducted on the report texts. Through keyword analysis, the focus of concern of the two companies is understood. At the discourse practice level, the process of report generation and understanding is explored in terms of data sources and case use. At the social practice level, the performance of the two companies in different cultural contexts is analysed to understand how cultural differences affect the companies’ image building. The study shows that AbbVie’s and Hengrui’s corporate images have common points: focusing on management, actively fulfilling social responsibility, and pursuing sustainable development. Whereas AbbVie is more inclined to show global vision and inclusiveness, Hengrui emphasises local authority and patriotism. By analysing the discourses of the two corporations’ CSR reports, it is possible to gain a deeper understanding of how corporations shape their corporate image through linguistic strategies and to reveal the cultural factors behind the differences in corporate image.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".