A Study of Mainstream Malaysian Media’s Attitude and Its Changes on China’s Belt and Road Initiative: A Corpus-based Critical Discourse Analysis (2018-2023)
Bibliographic record
Abstract
The period of 2018 to 2023 saw both four changes in the Malaysian government and a geopolitical game between great powers in the Asian-Pacific region. Especially, 2020 saw both change in the Malaysian government as well as the outbreak of COVID-19—inevitably affecting Malaysia’s attitude towards the Belt and Road Initiative (BRI) both politically and economically. Hence, this study collects BRI reports from the mainstream Malaysian media—New Straits Times (NST)—from 2018 to 2023. Using corpus-based critical discourse analysis as the research framework, this study builds two target corpora for the periods of 2018-2019 and 2020-2023, respectively. Through comparative analysis of the reports amount, thematic terms, and concordance lines, this study examines NST’s attitude towards the BRI and changes of attitude. The research findings firstly indicate a dynamic upward trend in NST’s coverage of the BRI; secondly, the content of NST’s reports shows a trend of diversification; and thirdly, while it expresses concerns about issues such as China’s rise and challenges to security in the Southeast Asian region, NST’s attitude towards the BRI remains positive overall, with no significant changes. This study explores Malaysia’s attitudes towards the BRI from the perspective of news discourse analysis—providing both a perspective for enhancing mutual understanding between China and Malaysia, and further understanding of the status of China-Malaysia cooperation under the BRI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".