Spectacular Technology, Invisible Harms: Witnessing Techno-science on Waste Tours in China
Bibliographic record
Abstract
Investment in science, technologies, and infrastructures has been a critical aspect of China’s development strategy since the early 2000s. China’s national policies designated waste-to-energy (WtE) incinerators as the dominant end-of-life technology to bring about a form of modern and sustainable waste treatment that can turn waste into energy while eliminating pollution. Amid rising citizen skepticism over the safety and efficacy of this technology in China and elsewhere, this article examines the genre of the orchestrated waste tour, which seeks to place the public as witnesses to state performances of technological improvement. Tours to waste facilities illuminate the generic conventions and strategies that China’s late-socialist mode of green techno-scientific governance relied on to legitimize its achievement of environmental improvement. Tours did not produce passive observers. Through an in-depth discussion of a waste tour in Guangzhou, this paper documents that opportunities for firsthand encounters of WtE incinerators provided a forum for those suspicious of the state’s claims of techno-science to form counternarratives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".