Bibliographic record
Abstract
I WILL ADDRESS the ethics of placebo‐controlled trials in general and will not enter into the specifics of which sorts of trials may or may not ethically use a placebo control in osteoporosis. The reasons for this are 2‐fold. First, and most obviously, I am not an expert in the treatment of osteoporosis. Moral actions for the use of placebo controls turn on facts regarding the standard of care, in this case, for the treatment of osteoporosis. Experts in the field are in the best position to determine such facts, and the moral actions for osteoporosis trials follow by deductive logic from the framework I will outline. Second, the term “ethics” and what it might mean has come up repeatedly during this conference, even in the most scientific of presentations. We have heard the ethics of placebo‐controlled trials analyzed according to “use,” “social use,” and “economic analysis.” These are, I contend, wrong‐headed ways of approaching the ethics of research. I believe a paper that steps back from the particulars of this debate and addresses the ethics of research at a more conceptual level will be of greatest use.
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 | 0.511 | 0.632 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.030 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".