Bibliographic record
Abstract
As everybody knows, advances in medicine and medical technology have brought enormous benefits to, and created vexing choices for, us all – choices that can, and occasionally do, test the very limits of thinking itself. As everyone also knows, we live in the age of consultants, i.e., of professional experts who are ready, willing, and able to give us advice on any and every conceivable question. One such consultant is the medical ethics consultant, or the medical ethicist who consults. Medical ethics consultants involve themselves in just about every aspect of health care decision making. They help legislators and judges determine law, hospitals formulate policies, medical schools develop curricula, etc. In addition to educating physicians, nurses, and lawyers, amongst others, including medical, nursing, and law students, they participate in clinical decision making at the bedside.
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 | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.033 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".