The Better I Can Be: In Defence of Human Enhancement for a New Genetic Equality
Bibliographic record
Abstract
The main objection to genetic enhancement is that it will create a “genetic apartheid,” deepening existing inequalities. This paper offers considerations that can weaken the inequality argument against genetic enhancement. First, I question the dichotomy of treatment versus enhancement since the differences between the two are unclear. Second, I argue that human enhancement is part of human nature and that there is no sound reason to accept it in other domains while rejecting it in genetics. The paper also demonstrates that inequality is present in every dimension of society, that “God-given” genetics is profoundly unequal, and that genetic enhancement can operate as a mechanism by which a new genetic equality can be achieved. However, the paper underlines that genetic equality is not, per se, a value to which we ought to aspire if it leads us to a uniform community of downsized human beings. Genetic equality is only valuable if it enhances humankind in general.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".