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The Solidarity and Health Neutrality of Physicians in War & Peace

2017· article· en· W2575492840 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLoS Currents · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsSolidarityNeutralityCommitHealth careNationalityDutyLawPoliticsInternational humanitarian lawBusinessPublic relationsPolitical scienceMedicineHuman rights

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.295
GPT teacher head0.523
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it