Many Actors, Many Policies: Regulating Assisted Reproductive Technologies in Canada and the United States
Bibliographic record
Abstract
While political scientists have turned a scholarly eye towards assisted reproductive technologies (ARTs) in recent years, they have been ineffective at both defining the object of study and determining those responsible for creating public policy. This paper seeks to provide an antidote to both scholarly shortcomings. First, I develop a six-part typology of what is included within the term “ART policy”: assisted conception, surrogacy, embryonic research, human cloning, offspring engineering, and parentage. Second, I place a greater emphasis on the extent to which courts, subnational governments, and medical self-regulatory organizations are responsible for creating ART policy. Using Canada and the United States as case studies, I apply this framework to re-evaluate whether each country should be described as having a restrictive, intermediate, or permissive ART regime. My analysis suggests that substantial jurisdictional variation within each country often stems from judicial decisions related to surrogacy and gamete donors. Given the level of ART policy variation, it is more useful to determine the permissiveness of each subnational jurisdiction’s regime, rather than identifying each country as a whole.However, it is also important to note that policy harmonization can occur from an unlikely source: national specialist medical organizations, such as the Society for Assisted Reproductive Technologies (SART) in the United States and the Society of Obstetricians and Gynaecologists of Canada (SOGC). While non-statutory medical guidelines do not have the same force as criminal prohibitions, there is growing evidence that medical professionals in both countries are adhering to these guidelines. Yet the policy capability of medical self-regulatory organizations should not be overstated: using the six-part typology developed above, medical organizations are better equipped to create policy in certain subfields (assisted conception and offspring engineering) than others (surrogacy, embryonic research, human cloning, and parentage). This paper can thus offer a more precise framework for cross-national comparison of ART policy by introducing a mechanism to determine whether similar institutions produce similar policy outputs across jurisdictions. The six-part typology offers a better way to understand the “division of labor” in which ART policy regulators ought to engage; the examination of additional policymaking arenas adds other laborers to the mix.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".