The Solidarity and Health Neutrality of Physicians in War & Peace
Bibliographic record
Abstract
The wars in the Middle East have led to unprecedented threats and attacks on patients, healthcare workers, and purposeful targeting of hospitals and medical facilities. It is crucial that every healthcare provider, both civilian and military, on either side of the conflict become aware of the unique and inherent protections afforded to them under International Humanitarian Law. However, these protections come with obligations. Whereas Governments must guarantee these protections, when violated, medical providers have equal duty and obligations under the Law to ensure that they will neither commit nor assist in these violations nor take part in any act of hostility. Healthcare providers must not allow any inhuman or degrading treatment of which they are aware and must report such actions to the appropriate authorities. Failure to do so leads to risks of moral, ethical and legal consequences as well as penalties for their actions and inactions. There must be immediate recognition by all parties of the neutrality of health care workers and their rights and responsibilities to care for any sick and injured patient, regardless of their nationality, race, religion, or political point of view.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".