Digital activism and collective mourning by Chinese netizens during COVID-19
Bibliographic record
Abstract
This study examines the discursive practice of mourning and commenting by netizens on the final social media post made by Dr Li Wenliang, regarding it as a form of political participation and competitive discursive politics enacted in cyberspace. Discourse theory is applied to conduct discourse analysis on 4000 comments. We identified two strategies that netizens used to establish an alternative space for discourse. The first involved hidden protests expressed through multi-semantic mourning, avoiding suppression by indirectly challenging official authorities. Second, through engagement with microblogs, netizens applied personalized narratives to form a collective memory and a counter-memory space that departed from the official normative narrative. Discursive activities enacted by netizens stimulated the political agenda of resilient adjustment on the part of the authorities, leading the government to accept and incorporate public demands into policies through strategic rectification. These findings help to better understand the significant power of disorganized connective action that is reliant on affective citizens and the further development of regime resilience on the part of the Chinese political system in response to digital activities.
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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 | 0.001 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".