Human Germline and Heritable Genome Editing: The Global Policy Landscape
Bibliographic record
Abstract
Discussions and debates about the governance of human germline and heritable genome editing should be informed by a clear and accurate understanding of the global policy landscape. This policy survey of 106 countries yields significant new data. A large majority of countries (96 out of 106) surveyed have policy documents—legislation, regulations, guidelines, codes, and international treaties—relevant to the use of genome editing to modify early-stage human embryos, gametes, or their precursor cells. Most of these 96 countries do not have policies that specifically address the use of genetically modified in vitro embryos in laboratory research (germline genome editing); of those that do, 23 prohibit this research and 11 explicitly permit it. Seventy-five of the 96 countries prohibit the use of genetically modified in vitro embryos to initiate a pregnancy (heritable genome editing). Five of these 75 countries provide exceptions to their prohibitions. No country explicitly permits heritable human genome editing. These data contrast markedly with previously reported findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".