REPROGENETICS: LAW, POLICY, AND ETHICAL ISSUES
Bibliographic record
Abstract
List of contributors -- Preface -- Pt. I. The historical and regulatory landscape -- Ch. 1. On drawing lessons from the history of eugenics / Diane B. Paul -- Ch. 2. Governmental regulation of genetic technology, and the lessons learned / Julie Gage Palmer -- Ch. 3. Oversight of assisted reproductive technologies: the last twenty years / Andrea L. Bonnicksen -- Pt. II. Ethical issues in reprogenetics -- Ch. 4. Market transactions in reprogenetics: a case for regulation / Suzanne Holland -- Ch. 5. Stem cells, clones, consensus, and the law / Timothy Caulfield -- Pt. III. International regulation of reprogenetics -- Ch. 6. The governance of reprogenetic technology: international models / Lori P. Knowles -- Ch. 7. Regulating reprogenetics in the United Kingdom / Andrew Grubb -- Ch. 8. The evolution of public policy on reprogenetics in Canada / Patricia A. Baird -- Pt. IV. Regulating reprogenetics in the United States / Ch. 9. A brief history of public debate about reproductive technologies: politics and commissions / Kathi E. Hanna -- Ch. 10. Possible policy strategies for the United States: comparative lessons / Alison Harvison Young -- Ch. 11. The development of reprogenetic policy and practice in the United States: looking to the United Kingdom / Gladys B. White -- Ch. 12. Reprogenetics and public policy: reflections and recommendations / Erik Parns and Lori P. Knowles -- Index
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.033 | 0.030 |
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".