Value-Free Extradition? Human Rights and the Dilemma of Surrendering Wanted Persons to China
Bibliographic record
Abstract
A key tool for fighting crime, international extradition raises human rights concerns: the wanted person might suffer rights violations in the country to which they are extradited. How do countries balance the need to bring offenders to justice with the need to respect their rights? Conventional wisdom suggests that human rights concerns receive growing emphasis in extradition treaties, legislation, and case law. This article, however, shows that the commitment to human rights in extradition is quite shaky, even among countries that are strongly committed to human rights. This finding results from an analysis of the Australian and Canadian debates over the signing of extradition treaties with China. In Australia, government lawyers and the foreign ministry did not consider China's human rights record as an obstacle to extradition. In Canada, the government argued that extradition to China was consistent with human rights standards. In both countries, the interest in strengthening relations with China outweighed the commitment to human rights. Overall, this article advances our understanding of the status of human rights in criminal justice policy; it also contributes to the analysis of human rights engagement with China.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".