Bibliographic record
Abstract
Abstract In a recent article, Sven Nyholm argues that the use of biomedical enhancements (BE) in our romantic relationships would fail to secure the final value we attribute to love. On Nyholm's view, one thing we desire for its own sake is to be at the origin of the love others have for us. The satisfaction of this desire, he argues, is incompatible with the use of BE insofar as they are responsible for the attachment characteristic of love. In particular, the use of BE in order to create and sustain the sort of attachment characteristic of love would be less desirable than the creation and sustainment of it by more ordinary means. If one needs such enhancements in order for one's love to be created or sustained, then one's love is of lesser quality than the love we want. In this reply, I raise doubts about the argument.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.076 | 0.095 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".