Markets, Subsidies, Regulation and Trust: Building Ethical Understandings into the Market for Fertility Services
Bibliographic record
Abstract
This article uses the example of an Australian fertility clinic's efforts to recruit sperm donors from Canada to explore the issue of how ethical understandings can be built into market infrastructures. Australian fertility clinics lack sufficient sperm donors to meet current demand in part because of two ethical principles built into Australian law: a ban on payment for gametes and an insistence that sperm donor identity be available to the resulting child when the child reaches an appropriate age. The ethics oversight committee in New South Wales was willing to relax the first principle to permit the payment of substantial expenses to Canadian donors in order to uphold the second principle of mandatory identity disclosure. While it is still unclear whether these efforts will succeed in recruiting many donors, this article treats these practices as a case study in the creation of ethical understandings pertaining to the use of new technologies. Fertility services have been expanding as part of a global market in which the infertile can comparison shop on the internet, and arrange delivery of sperm through overnight express. At the same time, the use of such services is subject to a welter of conflicting, and often religiously driven, regulations. Under what circumstances are the resulting markets likely to lock in ethical norms, and under what circumstances are they likely to develop in accordance with the lowest common denominator of consumer preferences? This article identities four axes to examine in determining the efficacy of efforts to instill new ethical understandings. The first regulatory decision is the one to permit or ban the enterprise. We consider the circumstances in which bans are most likely to create unregulated underground markets that produce wholesale circumvention of the prohibition. The second is comparative regulation. Is regulation of the still nascent fertility industry engaged in a competition in laxity in which the jurisdiction that creates the most favorable regulatory environment captures the lion's share of the business? The third factor is subsidization. Access to subsidized health cares services is likely to persuade many to stay in their home countries if there is any possibility of securing services there. Finally, we consider the role of trust in facilitating acceptance of new norms. Canadians, for example, may be more comfortable than Australians with identity disclosure if they have greater confidence, if only because of distance, that the child will not show up on their doorsteps.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".