Bibliographic record
Abstract
The current debate on the ethics of cloning humans is both uninspired and uninspiring. In large measure this is because of mistakes that permeate the discourse, including the mistake of thinking that cloning technology is strictly a reproductive technology when it is used to create whole beings. As a result, the challenge this technology represents regarding our understanding of ourselves and the species to which we belong typically is inappropriately downplayed or exaggerated. This has meant that important (albeit disquieting) societal issues and species-type concerns have not been fully explored. This paper, intended as a corrective, suggests that we take an alternate view of human cloning as both an enhancement and a reproductive technology. This proposed shift in the framework for analysis counters the current narrow framing of the issues and introduces new questions about the prospect of modifying the species.
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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.101 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".