Bibliographic record
Abstract
Last year, Angilee Shah wrote a review at China Beat of Oliver August’s Inside the Red Mansion. The review inspired Simon Fraser University Professor Jeremy Brown to assign the text to a class and he recently invited the book’s protaganist, Lai Changxing, to join his class for a day. Brown and one of his students provide an account of the day’s visit below (for a write-up in Chinese, see this report at The Global Chinese Press). A few days before National Day, Lai Changxing joined our fourth-year Chinese history class at Simon Fraser University. For almost three hours China’s most wanted man answered (and dodged) a wide range of tough questions from students, all of whom had read Oliver August’s Inside the Red Mansion, a book about shady ties between officials and businesspeople in Xiamen, where Lai made his millions and later became enmeshed in a corruption scandal that led him to flee to Canada. Lai lives in Vancouver and sent two of his sons to SFU. What did our class learn from him? In his responses to student questions, Lai alternated between innocent charm and aggrieved combativeness. He denied giving officials cash-filled briefcases and providing them with modern-day concubines. But he admitted that he actively sought out and took advantage of loopholes. In order to avoid customs duties when importing oil and luxury cars to China, Lai said that he had his oil tankers unload when nobody was watching. His overarching goal was to make more money, he said, so he was constantly looking for opportunities. When local officials announced that new businesses would be exempt from taxes for three years, Lai opened a series of ventures, and then shut them down and changed their names before they hit the three year mark, managing to perpetually avoid taxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".