Super-Soldiers Revisited: The Ethics of Using Military Personnel as Research Subjects
Bibliographic record
Abstract
A fascinating topic that has not been recently revisited by bioethics is the ethics of human experimentation within the military context, in keeping with the pace of modern technology development. Many research innovations stem from military research, where emerging technologies are first applied in the field then eventually repurposed for civilian contexts. This commentary presents an ethical framework for the usage of military personnel as research subjects, within the context of modern military research such as epigenetic technology development in soldiers. Tensions are raised between existing military versus civilian bioethical frameworks for human experimentation and compared to risk-benefit assessments within and beyond the military context. A harmonized ethical framework is proposed for the use of research subjects within the military. The pace of modern scientific research, particularly in genomics, poses new ethical considerations of genetic profiling, consent, risk, and data privacy that urges a timely revisit of military bioethics.
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 | 0.147 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.021 | 0.029 |
| 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".