You Can’t Put the Genie Back in the Bottle: Rights and Liabilities of Egg Donors in the Cyberprocreation Era
Bibliographic record
Abstract
The earth is, possibly, more than four billion years old; but a great deal can happen in ten. In 1999, a married couple advertised in Harvard and Princeton campus newspapers offering $50,000 for the eggs of an Ivy League egg donor who was 5’10" or taller and scored over 1400 on her S.A.T.s. They wanted a donor with those characteristics and campus newspapers probably seemed the most logical way to find one. At that time, only 40% of the American population sixteen years and older accessed the Internet; today over 73% do. An October 14, 2009 Google search generated about 326,000 hits in response to "egg donor wanted" in 0.13 seconds and there is little doubt that donees will find donors online in the future. This article contends that while the Internet increased the availability of, and the market for, donor eggs to a larger audience than ever envisioned, it also created significant and unimagined legal concerns for egg donors. You can’t put the genie back in the bottle.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".