Online Nationalism in China: Weibo reactions to the detention of Huawei CFO Meng Wanzhou
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it