Beyond Human Subjects: Risk, Ethics, and Clinical Development of Nanomedicines
Bibliographic record
Abstract
Like all policies, contemporary human research policies are the product of their history. The scandals and traumas motivating their creation — the Nazi doctors trials, Tuskegee, the Milgram experiment on obedience — however different in their particulars, all share a common narrative: a scientist, pursuing valued social ends, runs roughshod over the personal interests of disadvantaged human subjects. From the Nuremberg code through the latest revisions of the Declaration of Helsinki, research ethics policies have sought to erect a sphere of protection around the latter. As a consequence of this history, all major policies start with a well-rehearsed model of human investigations. Clinical research is viewed as an encounter between investigators and volunteers. The clinical investigator is given certain duties. The human volunteer has certain moral entitlements. What is ethically at stake in human investigations inheres in the nature and quality of the interactions between investigators and volunteers. These interactions involve an asymmetry because the investigator has privileged knowledge and influence.
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 | 0.129 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.108 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".