Genre Analysis of Saudi Universities’ Websites: Analysis of Rhetorical Moves and Discursive Strategies for Marketization Purposes
Bibliographic record
Abstract
The field of education has experienced numerous shifts, including internationalization, greater competitiveness and collaboration, and globalization. Today, higher education branding has become a common trend. To differentiate themselves in the business world, universities apply different branding techniques and this study aims to examine how some Saudi universities advertise themselves. Incorporating Critical Discourse Analysis (CDA), the study analyzes the “About Us” sections of seven Saudi universities’ websites to explore the rhetorical moves and discursive strategies employed for marketization purposes. The selected universities all featured in the top 1000 universities in the QS World University Rankings 2020. The results show diversity in these universities’ choices of implemented rhetorical moves and sub-moves. They used eight rhetorical moves and 13 sub-moves, with only one of these sub-moves occurring on all seven universities’ websites. The discursive strategies were employed to foster self-promotion, while the results demonstrated that the “About Us” sections of all universities were promotional. Finally, some recommendations are provided for universities for marketization purposes if they want to be global and compete with other international universities in the higher education market, in addition to providing suggestions for future studies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".