Representation of China's Image on The Globe and Mail: A Corpus-Based Critical Discourse Analysis from the Perspective of van Dijk’s Ideology Square
Bibliographic record
Abstract
In early 2020, the outbreak of COVID-19 in Wuhan put China in the spotlight of international news. China’s development in the field of politics, society, and economics still occupies media attention in the context of the global public health crisis. Previous studies have shown that China’s image in the western media, including the Canadian media, adopted a generally negative tone. The objective of this thesis is to investigate China’s image in one of the mainstream Canadian media platforms, the Globe and Mail, during the whole epidemic year of 2020. This research is based upon news reports published in 2020 by the Globe and Mail and is conducted with the support of corpus linguistic methodology and with the guideline of Critical Discourse Analysis, especially van Dijk’s ideology square. In order to discern and identify the image of China as portrayed by the Globe and Mail, the underlying discourse strategies are analyzed. Findings show that “China”, “Chinese government”, and “Chinese citizens” are the main subjects that make up the broad concept of China’s image. And in China’s related news report in the Globe and Mail from 31 December 2019 to 31 December 2020, (1) rhetoric devices, including number game, news source selection, illustration, irony, implication, presumption, vagueness, comparison, and history as lesson, are employed to polarize the image of the Chinese government and the Canadian government. (2) China’s image in the Globe and Mail is generally negative, which can be described via four aspects: China as a threat, callous, dishonest/untrustworthy, and powerful (3) the China-related news as constructed by the Globe and Mail reflects its own particular national ideologies and interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".