Bibliographic record
Abstract
Deke Caiñas Gould (2021) argues that the possibility of future non-human-like minds who are not harmed by coming into existence poses a challenge to David Benatar's well-known Asymmetry Argument for anti-natalism. Since the good of these future minds has the potential to outweigh the current harms of human existence, they can be appealed to in order to justify procreation. I argue that Gould's argument rests on a fundamental misunderstanding of Benatar's argument. According to the Asymmetry Argument, if a person experiences any harm at all, then bringing them into existence is unjustified. It does not depend upon on-balance judgments about the relative harms and benefits of existence. It therefore remains impermissible to procreate right now in our world, regardless of the prospect of future humans contributing to the successful development of beings who are not harmed by existence. I conclude by addressing two alternate readings of Gould, which, for the sake of argument, permit such on-balance judgments, and show why they fail to rescue his case. Benatar's Asymmetry Argument might be unsound, but not for any reason identified by Gould.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.042 | 0.040 |
| Insufficient payload (model declined to judge) | 0.004 | 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".