Sorting Out Surrogacy: Three Considerations for Evidence-Informed Law Reform
Bibliographic record
Abstract
This article draws on novel survey research with surrogates in Canada—the Exploring Surrogates Experiences and Insights Survey—to examine three critical issues for potential law reform: (1) reimbursement and compensation (i.e., payment); (2) the work of surrogacy agencies; and (3) surrogacy arrangements involving foreign intended parents. Following a brief history of the regulation of surrogacy in Canada and a description of the survey’s methods, it explores each of these areas in turn, using the survey results to identify considerations for law reform. It argues that while the current legislative and regulatory framework largely addresses key issues related to commercialization and commodification, it does not adequately consider other crucial areas of concern, namely the influence of surrogacy agencies and the rise of transnational surrogacy, necessitating more comprehensive federal regulation.
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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 | 0.327 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.022 | 0.107 |
| Scholarly communication | 0.031 | 0.042 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.034 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 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".