Online Nationalism in China: Weibo reactions to the detention of Huawei CFO Meng Wanzhou
Bibliographic record
Abstract
This thesis investigates the topic of nationalism in Weibo posts that discuss the detention of Meng Wanzhou, Chief Financial Officer (CFO) of Huawei. After the arrest, Weibo users quickly connected this case with broader nationalist topics, resulting in different types of nationalist reactions. This study describes how these reactions reflect, create or shape a nationalist discourse. This was done in three parts: first of all, I examined how the countries of Canada, the US and China are described. The analysis reveals that the comments describe the US as the active culprit and Canada as a more passive, docile country. Secondly, the question was formulated as to how Meng was described, as a person, as CFO of Huawei, and as a Chinese, in order to gain more insight into how these different layers of her identity coincide or contrast. This part concludes that most commenters express their support for Meng, but that her wealth and unclarity regarding her citizenship can result in a decrease of support. Finally, I investigated the ways in which nationalism can be converted into action. It became clear how consumption and nationalism can be linked: many Weibo users suggested to initiate a boycott, mainly against Apple. Simultaneously, others also reflected on the efficacy of such measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".