The stem cell debate continues: the buying and selling of eggs for research
Bibliographic record
Abstract
Now that stem cell scientists are clamouring for human eggs for cloning-based stem cell research, there is vigorous debate about the ethics of paying women for their eggs. Generally speaking, some claim that women should be paid a fair wage for their reproductive labour or tissues, while others argue against the further commodification of reproductive labour or tissues and worry about voluntariness among potential egg providers. Siding mainly with those who believe that women should be financially compensated for providing eggs for research, the new stem cell guidelines of the International Society for Stem Cell Research (ISSCR) legitimise both reimbursement of direct expenses and financial compensation for many women who supply eggs for research. In this paper, the authors do not attempt to resolve the thorny issue of whether payment for eggs used in human embryonic stem cell research is ethically legitimate. Rather, they want to show specifically that the ISSCR recommended payment practices are deeply flawed and, more generally, that all payment schemes that aim to avoid undue inducement of women risk the global exploitation of economically disadvantaged women.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.024 | 0.019 |
| 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".