Bibliographic record
Abstract
Biomedical enhancement interventions require a departure from the justificatory routes typically available to biomedical research, focusing on making individuals “better than normal” instead of treating those who are impacted by illness and disease. Discussion of the ethical implications of such interventions has primarily been done in bioethics and political philosophy, but the questions concerning the ethical practice of enhancement are substantially different than those concerning the research. This thesis will focus on the ethical questions pertinent to biomedical enhancement research, including an examination of the arguments from enhancement opponents as applied to research ethics and an assessment of the values promoted through such research. Furthermore, it shall attempt to elucidate the values motivating biomedical enhancement research so as to better recognize the justifications behind exposing trial participants to health risks and to develop a strategy for mitigating practical problems linked to prioritization in enhancement and treatment research.
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 | 0.153 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.069 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".