Bibliographic record
Abstract
Comparative political science concerning assisted reproductive technologies (ARTs) has faced two problems: first, it has not adequately defined the field; second, it has focused almost exclusively on policy produced by national governments. This dissertation uses Canada as a case study to demonstrate how a new six-part typology for ART policy, coupled with a focus on multiple policymakers, can produce greater clarity. It uses historical institutionalism to trace Canadian policy, and finds that the 1993 Royal Commission on New Reproductive Technologies – a “critical juncture” – engaged in constitutionally unstable framing strategies that shaped policy for decades to come. The Commission framed certain practices and technologies by combining a pro-technology “medical-scientific” discourse with a “national” discourse to justify federal government intervention. These framing strategies were recreated in path-dependent ways leading up to the 2004 Assisted Human Reproduction Act. However, framing the positive aspects of ART policy from a primarily “medical-scientific” perspective deprived the federal government of much of the constitutional ammunition that would permit a “national” response. This constitutionally risky strategy was confirmed in 2010 when a majority of Supreme Court justices struck down much of the Assisted Human Reproduction Act for violating provincial jurisdiction. In the face of this sub-optimal policy outcome, provincial governments, courts, and medical organizations have engaged in considerable policymaking concerning surrogacy, parentage, and assisted conception policy. While much of Canadian ART policy constitutes a “patchwork” of prohibitions, regulations, common law rulings, and clinical guidelines, it is a mistake to call it “unregulated.” The Canadian case study provides several conclusions for comparative scholars. First, combining the six-part typology with a focus on multiple policymakers can better explain ART policy variation within and across regimes. Second, policy framing strategies must take into account existing institutional structures; how actors frame jurisdictional responsibilities can matter as much as how they frame policy content. Finally, scholars should pay more attention to how ideas become (or do not become) institutionalized. While policy framing can create ideational path dependence, the actual transfer from ideas to institutions often requires more than legislation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".