Digital Environmentalism: A Case Study of PM2.5 Pollution Issue in Chinese Social Media
Bibliographic record
Abstract
The emergence of social media over the last decade has substantially altered not only the means people communicate with each other but also the whole online ecosystems. For the common public in particular, social media enables and broadens the social conversation that anyone interested can engage in on urgent social problems such as environmental pollution. In China, the ever-thickening air pollution smothering most urban cities in recent years has provoked a nationwide discussion, and popular social media like Weibo has been fully utilised by various social actors to participate in this “green speak”. This paper examines the civil discourse about the deteriorating air pollution on China’s largest microblogging platform-Sina Weibo, and seeks to understand how different social actors respond to and reconstruct the reality. Through a discourse analysis aided by a text analytics/ visualisation software—eximancer, this paper investigates the civil discourse from three angles: the demographics, the discursive strategies and the potential social effect. The result suggests that proactive civil engagement in this issue has produced an environmental discourse with a wide range of topics involved, and that the benign interactions between social actors could give rise to a proactive interactional mode between Chinese state and civil society which would definitely be beneficial to the democratisation process in contemporary 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".