The Tyranny of the Gift: Sacrificial Violence in Living Donor Transplants
Bibliographic record
Abstract
Medical anthropology can bring to living donor transplant useful insights on the nature of gifting, family obligations, reciprocity and invisible sacrifice. Whereas, ethical reflections and debates on the marketing of tissues and organs, especially sales by living strangers, have proliferated to the point of saturation, the larger issue of the ethics of "altruistic" donation by and among family members is more rarely the focus of bio-ethical scrutiny and discussion today, though of course it was much debated in the early decades of kidney transplant. As the proportion of living over deceased donors (especially of kidneys) has increased markedly in the past decade, the time is ripe to revisit the topic, which I shall do via three vignettes, all of them informed by my 10 years as founding Director of Organs Watch, an independent, university-based, anthropological and ethnographic field-research and medical human rights project. Whereas living-related (altruistic) and living-unrelated (commercial) donation are often treated as very different phenomena, I will illustrate what social elements are shared. In both instances, paid kidney sellers and related donors, are often responding to family pressures and to a call to "sacrifice".
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".