Modular Ontologies for Genetically Modified People and their Bioethical Implications
Bibliographic record
Abstract
Participants in the long-running bioethical debate over human germline genetic modification (HGGM) tend to imagine future people abstractly and on the basis of conventionalized characteristics familiar from science fiction, such as intelligence, disease resistance and height. In order to distinguish these from scientifically meaningful terms like "phenotype" and "trait," this article proposes the term "persemes" to describe the units of difference for hypothetical people. In the HGGM debate, persemes are frequently conceptualized as similar, modular entities, like building blocks to be assembled into genetically modified people. They are discussed as though they each would be chosen individually without affecting other persemes and as though they existed as components within future people rather than being imposed through social context. This modular conceptual framework appears to influence bioethical approaches to HGGM by reinforcing the idea of human capacities as natural primary goods subject to distributive justice and supporting the use of objective list theories of well-being. As a result, assumptions of modularity may limit the ability of stakeholders with other perspectives to present them in the HGGM debate. This article examines the historical trends behind the modular framework for genetically modified people, its likely psychological basis, and its philosophical ramifications.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".