Preimplantation Genetic Diagnosis, Reproductive Freedom, and Deliberative Democracy
Bibliographic record
Abstract
In this paper I argue that the account of deliberative democracy advanced by Amy Gutmann and Dennis Thompson (1996, 2004) is a useful normative theory that can help enhance our deliberations about public policy in morally pluralistic societies. More specifically, I illustrate how the prescriptions of deliberative democracy can be applied to the issue of regulating non-medical uses of pre-implantation genetic diagnosis (PGD), such as gender selection. Deliberative democracy does not aim to win a philosophical debate among rival first-order theories, such as libertarianism, egalitarianism or feminism. Rather, it advances a second-order analysis that strives to help us determine what would constitute a reasonable balance between the conflicting fundamental values that arise in the context of regulating PGD. I outline a theoretical model (called the Reasonable Genetic Intervention Model) that brings these issues to the fore. Such a model incorporates the concern for both procedural and substantive principles; and it does so in way that takes provisionality seriously.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".