Is it unethical to publish data from Chinese transplant research?
Bibliographic record
Abstract
Non-consensual organ procurement from prisoners in China raises serious questions regarding the ethics of Chinese transplant research. In their article, published in this issue of JME , Higgins and colleagues address these questions through the lens of publication ethics. They argue that, ‘while there are potentially compelling justifications for use [of unethical research] under some circumstances, these justifications fail when unethical practices are ongoing’.1 Consequently, they recommend non-publication of Chinese transplant research and call for a mass retraction of the articles identified in their review.2 To support their argument, Higgins and colleagues appeal to internationally recognised guidelines from the WHO3 and the World Medical Association, which assert that ‘executed prisoners must not be considered as organ and/or tissue donors’ due to the inability to acquire valid consent.4 Failing to declare an immediate publishing moratorium for transplant research involving prisoners in China, they argue, ‘undermines efforts to stop transplant-related human rights abuses, taints the evidence base, and renders those who publish and use the research complicit in the continuing harm’.5 We agree with Higgins and colleagues that non-consensual organ procurement from prisoners in China is a crime against humanity. Ongoing human rights violations in Chinese prisons are well documented and universally condemned. We also agree that the subsequent use of data acquired from unethical research is morally complex. Nonetheless, Higgins and colleagues’ arguments leave us with three …
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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 | 0.429 | 0.679 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.029 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".