Bibliographic record
Abstract
What issues belong at the top of the agenda in bioethics? What important topics are commonly ignored? Does bioethics matter? As someone who writes about bioethics one of the lessons I have learnt is that the articles that typically attract the attention of editors and readers are the manuscripts addressing “sexy” topics. Ambitious researchers in bioethics know that if they want to obtain research funds and draw attention to their work they should focus on such topics as embryonic stem cell research, germ line gene therapy, and therapeutic and reproductive cloning. These topics practically sell themselves. Not long ago researchers examining ethical issues in medicine and health care had a different focus. In the 1980s and 1990s the study of ethical issues in palliative care generated hundreds of articles, as doctors, philosophers, and lawyers addressed such topics as the withdrawal of fluids and nutrition, …
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 | 0.174 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.091 |
| Scholarly communication | 0.042 | 0.060 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.073 | 0.118 |
| Insufficient payload (model declined to judge) | 0.009 | 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".