UW Political Scientist Discusses Corruption in China in Lawrence University Address
Bibliographic record
Abstract
Fourteen years to the day that the Chinese government used armed force against demonstrators in Beijing’s Tiananmen Square, a scholar of contemporary China discusses the societal problems widespread corruption is causing the country and the difficult choices facing China’s leaders in an address at Lawrence University. Melanie Manion, associate professor of affairs and associate director of the La Follette School of Public Affairs at the University of Wisconsin, presents “The Dilemma of Corruption in Mainland China: Saving the Country or Saving the Party?” Wednesday, June 4 at 4:30 p.m. in the Wriston Art Center auditorium. The event is free and open to the public. Despite more than two decades of reform efforts, Manion says China today ranks among the most corrupt countries in the world, with corruption reaching the highest level of government. According to Manion, Chinese leaders acknowledge the problem is more serious than at any time since 1949 when the communist assumed power and they view corruption as one of the greatest threats today to communist rule. On the anniversary of the 1989 massacre that ended the biggest anticorruption protest in Chinese communist history, Manion will examine how Chinese leaders have tried, largely unsuccessfully, to deal with the dilemma of the Chinese expression: “Don’t fight corruption and the country dies. Truly fight corruption and the communist party dies!” A member of the La Follette School faculty since 2000, Manion is the author of the forthcoming book, “Corruption by Design: Building Clean Government in Mainland China and Hong Kong” and the 1993 book “Retirement of Revolutionaries in China: Public Policies, Social Norms, Private Interests.” A graduate of Montreal’s McGill University, Manion studied for two years at the University of Peking before earning her master’s degree at the University of London and her Ph.D. in political science at the University of Michigan. Her visit is supported in part by the Henry M. Luce Foundation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".