Consent-Based Humanitarian Intervention: Giving Sovereign Responsibility Back to the Sovereign
Bibliographic record
Abstract
The repeated failure of the United Nations Charter regime to respond to humanitarian crises— and to prevent interventions outside the regime—has laid bare a conflict that lies at the heart of modern international law. This failure has revealed that the twin commitments on which the post-World War II international legal system has been built— sovereign rights and sovereign responsibilities— are often deeply at odds. The response of scholars to this tension has often been to choose sides in the fight. Scholars who place greater value on human rights than state sovereignty have sought to craft exceptions to the prohibition on the use or threat of force. Those who place greater value on sovereignty (and, they would argue, democratic rule of law), have rejected any humanitarian intervention not authorized by the Security Council as illegal and on occasion have portrayed the human rights movement as “anti-sovereigntist” and even “antidemocratic.” In this Article, we offer another way forward— one that aims to respect sovereign rights while helping states meet their sovereign responsibilities and thereby alleviate the tension between the twin commitments of the modern international legal system. Rather than seek to craft an exception to state sovereignty to meet humanitarian aims, we argue for empowering states to meet their sovereign responsibility through what we call “consent-based intervention.”
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".