Why the Government of Canada Won't Regulate Assisted Human Reproduction: A Modern Mystery
Bibliographic record
Abstract
The Canadian Assisted Human Reproduction Act (AHR Act), passed in 2004, prohibits both paying consideration to a surrogate mother and purchasing sperm and ova from a donor (sections 6-7). Both prohibitions are subject to section 12, which was intended to permit reimbursement of expenditures incurred by surrogate mothers and gamete donors and reimbursement for loss of work-related income for surrogate mothers. Remarkably, more than ten years after the AHR Act received Royal Assent, and in spite of repeated calls for greater legal clarity, Health Canada has not drafted regulations pursuant to section 12 of the AHR Act, which is not yet in force. In this paper, we speculate as to possible reasons why the Conservative government (2006-2015) did not draft regulations, and we explain in turn why each of the possible reasons for inaction is flawed. In light of our rejection of all of the reasons we could imagine, we argue that Health Canada should both explain and justify its failure to draft the regulations that would set the stage for Parliament to bring section 12 into force. It must do so if the federal government is to meet the AHR Act‘s goal of protecting children, women, and men engaged in, or affected by, surrogacy and third-party egg production.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.032 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.017 |
| 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".