AGGRESSION AS “ORGANIZED HYPOCRISY?” – HOW THE WAR ON TERRORISM AND HYBRID THREATS CHALLENGE THE NUREMBERG LEGACY
Bibliographic record
Abstract
Modern threats to international peace and security from so called “Hybrid Threats”, multimodal threats such as cyber war, low intensity asymmetric conflict scenarios, global terrorism etc. which involve a diverse and broad community of affected stakeholders involving both regional and international organisations/structures, also pose further questions for the existing legacy of Nuremberg. The (perhaps unsettling) question arises of whether our present concept of “war and peace”, with its legal pillars of the United Nations Charter’s Articles 2(4), 51, and the notion of the criminality of waging aggressive war based on the “legacy” of Nuremberg has now become outdated to respond to new threats arising in the 21st century. This article also serves to warn that one should not use the definition of aggression, adopted at the ICC Review Conference in Kampala in 2010, to repeat the most fundamental flaw of Nuremberg: ex post facto criminalisation of the (unlawful) use of force. A proper understanding of the “legacy of Nuremberg” and a cautious reading of the text of the ICC definition of aggression provide some markers for purposes of the debate on the impact of new threats to peace and security and the use of force in international law and politics.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".