Bibliographic record
Abstract
As the rapidly advancing possibilities of biotechnology have outstripped the adaptive capacity of current legal and ethical institutions, a vigorous debate has arisen that considers the boundaries of appropriate use of this technology, particularly when applied to humans. This article examines ethical concerns surrounding the development of markets in a particular form of human genetic engineering in which heterozygotes are fitter than both homozygotes, a condition known as heterozygous advantage. To begin, we present a generalized model of the condition, illuminated by the application to sickle-cell anemia. Next, we propose a typology of related markets, some of which are currently functioning with available products and services, and others that are widely viewed as imminent. We suggest the manner in which perverse incentives may arise for firms that market genetic intervention in circumstances where heterozygous advantage is possible. Finally, we propose that this misalignment of incentives with social welfare has arisen from both ill-conceived market intervention where markets are capable of achieving efficient outcomes and the lack of market intervention where markets have failed. We offer specific legal and regulatory approaches for reform.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".