Heart Transplants, Legislating Death, and Disruptive Anti-Apartheid Advocacy
Bibliographic record
Abstract
In December 1967, South African surgeon Christiaan Barnard astonished the world by becoming the first to successfully transplant a human heart. In the months that followed, a French Anti-Apartheid Committee proposed an international law to prohibit the harvest of prisoners' organs as French activists combined human rights and legal strategies to question the legality and ethics of Barnard's success. From a South Africa eager to present itself as modern and technologically advanced came an attempt to legislate use of organs from “living donors” in unspecified “authorized institutions.” In physicians' professional discussion of donor selection at an international heart transplantation symposium that Barnard convened in Cape Town, capital punishment figured as a proxy for race. Moving between South Africa, France and the United States, this article examines anti-apartheid activism, legislative innovation, and heart surgeons' attempts to normalize cerebral death and increase donors that followed the medical breakthrough in Cape Town, South Africa. It contributes to human rights history by showing how the French Anti-Apartheid Committee drew on transnational advocacy networks and legal strategies that predated the Universal Declaration of Human Rights, emphasizing collective over individual rights, deploying strategies of rupture, and actively pursuing a racially conscious, anticolonial solidarity.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".