Variations and voids: the regulation of human cloning around the world
Bibliographic record
Abstract
BACKGROUND: No two countries have adopted identical regulatory measures on cloning. Understanding the complexity of these regulatory variations is essential. It highlights the challenges associated with the regulation of a controversial and rapidly evolving area of science and sheds light on a regulatory framework that can accommodate this reality. METHODS: Using the most reliable information available, we have performed a survey of the regulatory position of thirty countries around the world regarding the creation and use of cloned embryos (see Table 1). We have relied on original and translated legislation, as well as published sources and personal communications. We have examined the regulation of both reproductive cloning (RC) and non-reproductive cloning (NRC). RESULTS: While most of the countries studied have enacted national legislation, the absence of legislation in seven of these countries should not be equated with the absence of regulation. Senator Morin was not correct in stating that the majority of recent legislation bans both RC and NRC. Recent regulatory moves are united only with regard to the banning of RC. While NRC is not permitted in seventeen of the countries examined, it could be permitted in up to thirteen countries. CONCLUSIONS: There is little consensus on the various approaches to cloning laws and policies, and the regulatory position in many countries remains uncertain.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".