Bibliographic record
Abstract
East Asian economic success stories-Japan, the "four tigers" (South Korea, Hong Kong, Singapore, and Taiwan), and most recently China-have brought world attention to the region.However, economic success has also brought pollution.East Asia is one of the world's regional-scale pollution hot spots.East Asian pollution affects local, regional, and global environments.It is critical, therefore, to understand the sources of pollution in East Asia and the efforts to control them.Michael Rock's short (about 200 pages) book opens a window onto one understudied dimension of East Asian pollution-industrial pollution management in East Asia's newly industrializing economies (NIEs).The book offers case studies of industrial pollution management in six East Asian NIEs-China, Indonesia, Malaysia, Singapore, Taiwan, and Thailand.Rock not only draws lessons from his case studies, but also, to his credit, aims to balance the highly negative image of pollution in East Asia with "success stories."He documents positive and innovative experiences, such as Taiwan's Flying Eagle Project in which helicopters were used to respond to citizen complaints about factory emissions, Indonesia's Proper Prokasih program in which a simple color-coded rating system for BOD (biological oxygen demand) of emissions to rivers was used in conjunction with public disclosure to push voluntary cleanup of Indonesia's rivers, and China's Urban Environmental Quality Examination System (UEQES) index in which a public rating of cities' overall environmental quality has encouraged competition among local ofªcials to clean up their cities.Rock's central questions are: Why did the six NIEs originally choose different industrial pollution management strategies, and how and why have these strategies changed over time (between roughly 1970 and 2000).To answer these questions, he constructs a picture based on the meager literature on the six NIEs' environmental regulatory agencies, a few empirical studies on their industrial pollution management practices, and his own in-depth interviews and analysis of pollution statistics.In addition, he employs Stephen Haggard's theoretical framework (used to explain economic development, and outlined in Pathways from the Periphery, University of Cornell Press, 1990) to structure his analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.396 | 0.339 |
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".