The Marketization of Higher Education Discourse: A Genre Analysis of University Website Homepages in China
Bibliographic record
Abstract
The past three decades have witnessed the growing influence of market forces on higher education, resulting in what is defined by Fairclough (1993) as the marketization of academic discourse. The present study attempts to examine the effect of such trend on university website homepages in China, which is an under-researched genre of higher education discourse. By applying the Critical Discourse Analysis (CDA) and genre analytical approach, this article describes the generic characteristics of the “About Us” section in five university website homepages, analyzing the structural organization, rhetorical moves, communicative purposes as well as the discursive strategies used in the text. Research shows that authoritative discourse forms the key note in this genre, a reflection of the centralized operation of Chinese universities. Meanwhile, the existence of conversational discourse reveals the university’s endeavor to establish a friendly relationship with the prospective students. Furthermore, promotional elements in terms of both contents and linguistic choices have been employed to help construct a positive image of universities to stand out in the stiff competition in today’s higher education market in China.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| 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".