Thinking of anti-incineration protests in strategic action fields: three case studies in mainland China
Bibliographic record
Abstract
Local protests against municipal solid waste (MSW) incineration have become intractable problems in metropolitan areas worldwide. Based on the theory of strategic action fields, we adopt a comprehensive perspective to understand how controversies surrounding MSW incineration exert influence over the dominant order within the field. Adopting a qualitative research method, we conducted field research in three Chinese cities where proposals for incineration plants have aroused disputes between different categories of actors. The collected empirical data consist of 42 semi-structured interviews and materials provided by interviewees. By examining the dynamic process of protests between challengers and incumbents, we found that skilled actors fight over a meso-level social order – the waste disposal industry and waste management policies – through competition and cooperation. We also found a mix of instrumental and existential motivations in their involvement in the conflict. Our findings deepens understanding of contentions regarding waste management, thereby enriching the existing literature. In a broader sense, our analysis contributes to the discussion of how actors occupying different positions compete for dominance in a specific field and can succeed, under certain circumstances, in shaping social action.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".