"Living Cadavers" in Bangladesh: Ethics of the Human Organ Bazaar
Bibliographic record
Abstract
The “miracle” success of transplant technology, alongside the commercialization of health care, and the increasing polarization between rich and poor have created the conditions for an illegal but thriving trade in human body parts. Based on 15 months of challenging fieldwork, my research examines the ethics of the organ bazaar, particularly the experiences of 33 kidney sellers living in Bangladesh. On the underground bazaar, not only human kidneys but also livers and corneas are advertised for sale. Recipients, sellers, and brokers regularly post newspaper advertisements to buy and sell organs. The average price for a kidney is US $1,500 in Bangladesh, a country where 78% of people live on less than $2 a day. My research examines serious ethical questions, such as these: Is it right to purchase an organ, even if the organ sought provides longevity? Is the sale of one’s organ a justifiable means of fighting poverty? These questions allow me to examine the ethics of harvesting organs, particularly from the bodies of impoverished people.\nNarrating the victims’ deeply moving testimonies, my ethnography reveals how organ buyers (both recipients and brokers) tricked and pressured Bangladeshi poor into selling their kidneys. In the end, these sellers were brutally deprived and deceived, and their suffering was extreme. In the post-vending period, sellers’ health, economic, and social conditions significantly deteriorated, yet none of them received the promised post-operative care—not even one appointment. \nMy research therefore concludes that organ commodification is serious structural violence against the poor, at the terrible cost of harm and suffering to them. Examining the organ market proposition, I argue that the resulting violence and injustice against the poor provide a hefty reason to rebut this trade. Bangladeshi kidney sellers also stood up against organ commodification, speaking out about their suffering, and about various detrimental and unethical outcomes incurred in this deal. My research aims to offer insights to bioethics and to broaden the debate on human rights by exposing how technological advancement, structural violence, and grinding poverty intersect in the violation of justice to the poor, turning them into “living cadavers.”
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".