The plurality of norms that factor into Canadians’ decisions to engage in transnational egg transactions
Bibliographic record
Abstract
Egg donation is regulated differently in countries around the world. In Canada, federal law – the Assisted Human Reproduction Act – prohibits paying egg providers. As a result, many Canadians are engaging in a grey market for eggs or are pursuing transnational egg transactions by traveling to countries with more permissive laws, or having eggs shipped to Canada. In this paper I rely on interview data with 18 Canadian intended parents and 16 Canadian egg providers – many of whom had traveled abroad for egg transactions – to understand how intended parents and egg providers decide how they will pursue egg transactions; specifically, why so many Canadians choose to engage in transnational egg transactions. I use Brian Z. Tamanaha’s theory of systems of normative ordering, combined with Paul Schiff Berman’s cosmopolitan pluralism, as a framework to reveal the plurality of norms and other factors that weigh into this decision-making and that help reveal the answer to the “why.” Ultimately, I illustrate how intended parents and egg providers are impacted by sometimes clashing norms from official legal systems, economic/capitalist systems, and customary/cultural systems. These norms, along with moral and practical concerns, shape the decisions of intended parents and egg providers.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.034 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".