COVID-19, Anti-Intellectualism, and Health Communication: Assessing the Chinese Social Media Platform Sina Weibo
Bibliographic record
Abstract
In the digital media era, new media platforms have become the main channels for transmitting medical and health information in China. However, anti-intellectualism limits the effectiveness of disseminating health information. Therefore, in China, the government and health departments have made efforts to determine how to control anti-intellectualism to effectively disseminate medical and health information, given the situation of a global pandemic and its counter-measures. Against this backdrop, this study applied textual analysis to explore the manifestations of anti-intellectualism in network platforms. The key findings indicate that the irrational behavior of anti-intellectuals is manifested in emotional dominance, abusive behavior, overconfidence and trusting rumors. Based on these results, the authors propose some measures to balance the relationship between anti-intellectualism and health communication. The findings of the study have significant implications for improving the effectiveness of health communication in China.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".