Bibliographic record
Abstract
This paper presents the real case of a military surgeon who is the only one working at a small hospital in Iraq. The military surgeon can only operate on one wounded soldier due to limited medical resources. The first wounded soldier to arrive is the enemy. The second wounded soldier to arrive shortly after the enemy is a compatriot. Both soldiers will die without lifesaving surgery. The military surgeon is ordered by his superior not to operate on the enemy. Under the Geneva Conventions, physician-soldiers are legally required to give medical attention impartially. The only exception is urgent medical need. Both soldiers, friend and foe, have an urgent medical need. Dual-loyalty dilemmas such as this one can arise for military medical practitioners when loyalty to patients comes into conflict with loyalty to third parties such as the state. In this paper, several solutions to the dual-loyalty dilemma are considered and rejected. Solutions to the dual-loyalty dilemma ultimate fail because they rest on the physician-as-healer model which grounds contemporary medical ethics. The view that the ultimate objective of physicians and medicine is winning battles is defended. Physicians are non-neutral and partial fighters who sometimes must do harm. Medicine is a weapon that physicians use to fight an enemy. The only relevant differences between a soldier and physician are the kind of enemy, location of the enemy, and the type of weapons used against the enemy. The paper concludes that physician-soldiers are doctors with borders. There is no dual-loyalty dilemma on the physician-as-fighter model. The military surgeon should obey his orders and not operate on the enemy. Implications of the physician-as-fighter model for mass casualty triage and physician-soldier participation in non-lethal weapons development are considered.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 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".