Legal and ethical aspects of anonymous sperm donation with focus on the Canadian legal order
Bibliographic record
Abstract
Nowadays, anonymous sperm donation is indeed a globally discussed topic: the popularity of the use of assisted reproduction technology for the purpose of conception is directly proportional to increasing infertility and technological progress. Also, the duration of its use already started to show, with the first generation of opinionated anonymous donor children growing up. The aim of my work is to analyze what are the today's Western society's current ethical and legal views on the anonymous sperm donation and its alternatives, as well as what they should be, and to demonstrate these on the example of Canada, United Kingdom and Australia. In the first two chapters, I outline the context of ethical and legal thinking about anonymous sperm donation: I analyze the concept and implications of the infertility, as well as the development and types of methods of assisted reproduction. Further, I proceed from the basis of ethical reasoning and the determination whether the right to know one's origins, implying the right to know the circumstances of one's conception, exist, and its competition with the rights of parents and donors in the third chapter, to its legal grounds. First, in the fourth chapter, I deal with legislation on the right to know one's origins and other related rights in the international...
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".