Bibliographic record
Abstract
Abstract The sequencing of the human genome and advances in gene therapy and genomic editing, coupled with embryo selection techniques and a potential gerontological intervention, are some examples of the rapid technological advances of the “genetic revolution.” This article addresses the methodological issue of how we should theorize about justice in the genomic era. Invoking the methodology ofnon-ideal theory, I argue that theorizing about justice in the genomic era entails theorizing about (1) the new inequalities that the genetic revolution could exacerbate (e.g., genetic discrimination, disability-related injustices, and gender inequality),and(2) those inequalities that the genetic revolution could help us mitigate (e.g., the risks of disease in early and late life). By doing so, normative theorists can ensure that we develop an account of justice that takes seriously not only individual rights, equality of opportunity, the cultural and sociopolitical aspects of disability, and equality between the sexes, but also the potential health benefits (to both individuals and populations) of attending to the evolutionary causes of morbidity and disability.
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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.096 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| 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".