Bibliographic record
Abstract
What we think about equality as a value will influence how we think genetic interventions should be regulated. In this paper, I utilize the taxonomy of equality put forth by Derek Parfit and apply this to the issue of genetic interventions. I argue that Telic Egalitarianism is untenable and that Deontic Egalitarianism collapses into the Priority View. The Priority View maintains that it is morally more important to benefit those who are worse off. Once this precision has been given to the concerns egalitarians have, a number of diverse issues must be considered before determining what the just regulation of genetic interventions would be. Consideration must be given to the current situation of the current situation of the least advantaged, the fiscal realities behind genetic interventions, the budget constraints on other programs egalitarians believe should receive scarce public funds and the interconnected nature of genetic information. These considerations might lead egalitarians to abandon what they take to be the obvious policy recommendations for them to endorse regarding the regulation of genetic therapies and enhancements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".