Human rights violations in organ procurement practice in China
Bibliographic record
Abstract
BACKGROUND: Over 90% of the organs transplanted in China before 2010 were procured from prisoners. Although Chinese officials announced in December 2014 that the country would completely cease using organs harvested from prisoners, no regulatory adjustments or changes in China's organ donation laws followed. As a result, the use of prisoner organs remains legal in China if consent is obtained. DISCUSSION: We have collected and analysed available evidence on human rights violations in the organ procurement practice in China. We demonstrate that the practice not only violates international ethics standards, it is also associated with a large scale neglect of fundamental human rights. This includes organ procurement without consent from prisoners or their families as well as procurement of organs from incompletely executed, still-living prisoners. The human rights critique of these practices will also address the specific situatedness of prisoners, often conditioned and traumatized by a cascade of human rights abuses in judicial structures. CONCLUSION: To end the unethical practice and the abuse associated with it, we suggest to inextricably bind the use of human organs procured in the Chinese transplant system to enacting Chinese legislation prohibiting the use of organs from executed prisoners and making explicit rules for law enforcement. Other than that, the international community must cease to abet the continuation of the present system by demanding an authoritative ban on the use of organs from executed Chinese prisoners.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".