Diplomatic dissonance: how Canadian newspapers cover China amidst changing relations
Bibliographic record
Abstract
This study examines the portrayal of Canada-China relations in three prominent Canadian newspapers—the <i>Toronto Star</i>, the <i>Globe and Mail</i>, and the <i>National Post</i>—between 2016 and 2023, a pivotal period marked by significant diplomatic tensions. By applying computational textual network analysis to a corpus of 2,000 news articles, the study reveals that the arrest of Huawei executive Meng Wanzhou in December 2018 marked a turning point. This event led to a notable increase in the salience of negative media frames concerning China, with negative sentiments in coverage of China rising from 58% in 2016 to a peak of 76% in 2018. Overall, the Canadian media’s portrayal of China has been predominantly negative between 2016 and 2023, reflecting Western political-media elites’ concerns about China’s expanding global influence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.126 | 0.002 |
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; both teacher heads agree on what is shown here.
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".