Human cloning laws, human dignity and the poverty of the policy making dialogue
Bibliographic record
Abstract
BACKGROUND: The regulation of human cloning continues to be a significant national and international policy issue. Despite years of intense academic and public debate, there is little clarity as to the philosophical foundations for many of the emerging policy choices. The notion of "human dignity" is commonly used to justify cloning laws. The basis for this justification is that reproductive human cloning necessarily infringes notions of human dignity. DISCUSSION: The author critiques one of the most commonly used ethical justifications for cloning laws - the idea that reproductive cloning necessarily infringes notions of human dignity. He points out that there is, in fact, little consensus on point and that the counter arguments are rarely reflected in formal policy. Rarely do domestic or international instruments provide an operational definition of human dignity and there is rarely an explanation of how, exactly, dignity is infringed in the context reproductive cloning. SUMMARY: It is the author's position that the lack of thoughtful analysis of the role of human dignity hurts the broader public debate about reproductive cloning, trivializes the value of human dignity as a normative principle and makes it nearly impossible to critique the actual justifications behind many of the proposed policies.
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 | 0.046 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.082 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.031 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".