It's all relative: The origins, legal character and normative content of the humanitarian principles
Bibliographic record
Abstract
Abstract Analyses of the humanitarian principles of humanity, neutrality, impartiality and independence often focus on the principles’ meanings and/or the challenges of applying them in practice. This article, by contrast, steps back to address foundational but somewhat neglected questions about whether these principles can accurately be designated “the” humanitarian principles; about how they came to govern the whole humanitarian sector; about their legal character and normative content; and, more fundamentally, about whether the principles can even have objective character and content. It begins by defining “humanitarian principles” and determining whether and on what basis certain principles constitute “the” humanitarian principles. The article then traces the history of how the principles came to govern the International Red Cross and Red Crescent Movement and diffused from there to non-governmental organizations and the United Nations system. It then analyzes the principles’ legal character and normative content for each of the above-mentioned categories of actor plus States, demonstrating that the principles do not – and, legally, cannot – have fixed legal character and normative content. While humanitarian actors share common understandings of the principles, legally the character and content of each principle flows from its source for the actor in question.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".