Future People as Future Victims: An Anti-Natalist Justification of Longtermism
Bibliographic record
Abstract
Abstract In this paper, I propose a refined version of Seana Shiffrin’s consent argument for anti-natalism and argue that longtermism is best justified not through the traditional consequentialist approach, but from an anti-natalist perspective. I first reformulate Shiffrin’s consent argument, which claims that having children is pro tanto morally problematic because the unconsented harm the child will suffer could not be justified by the benefits they will enjoy, by including what I call the trivializing requirement to better accommodate various criticisms. Based on this iteration of anti-natalism, I argue that future generations should not be seen as far away strangers who are merely anonymous bearers of well-being, but rather as collective victims of the wrongful acts of procreation. As a result, anti-natalism provides us with a rational ground to put a key moral priority on improving the future, not only as restitution to future generations for the unconsented harm imposed on them, but also as part of a long-term effort to nullify the anti-natalist criticism, since the consent argument would no longer apply if our society eventually becomes so utopian that the positive aspects of the average person’s life vastly outweigh its negative aspects.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".