Bibliographic record
Abstract
For more than ten years, China has been unsuccessful in requesting Canada to extradite Lai Changxing, who has been dubbed as “China’s most wanted man.” While many Chinese criticized Canada for its insensitiveness to China’s interests in combating corruption, Canada is in fact in a difficult position. As the Los Angeles Times outlined, Lai’s case has created a conundrum for Canada: “[i]f he goes, he is likely to face a firing squad. If he stays, Canada risks becoming a magnet for criminal refugees.” This paper proposes conditional extradition as a possible solution to this tough case. It first provides a general introduction to the current state of international extradition law. It then summarizes the key facts of Lai case. Part III of the paper analyzes Canada’s dilemma in dealing with the case. Finally, the paper proposes conditional extradition as a possible solution to the case, demonstrating why and how this approach might be feasible.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".