Global Trade and Assisted Reproductive Technologies: Regulatory Challenges in International Surrogacy
Bibliographic record
Abstract
Lawyers (and others) tend to look to the law to resolve disputes and to create certainty about the rights and responsibilities of parties to relationships. There is a particularly acute need for certainty in the context of global trade in surrogacy services, both because of the number of parties who may be involved in creating familial relationships and because of the vulnerabilities created as a result of surrogacy arrangements. Participants in the Global Health Challenges conference (on which this special issue is based) were invited to consider to what extent law is implicated in global health challenges — both in terms of how law might help to resolve the challenges, and (as is particularly of interest in international surrogacy), how law might contribute to or create these challenges.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.034 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".