The "War on Terror" and the "War of Terror": Nomadic Multitudes, Aggressive Incumbents, and the "New" International Law: Prefactory Remarks on Two "Wars"
Bibliographic record
Abstract
This article contrasts and compares the war on terror and the war of terror in the wake of, and before, 11 September 2001. The philosophical underpinnings involved in defining "terrorism" are analyzed in the context of the United States' war on terrorism and related wars of terrorism, such as the 1998 World Islamic Council's fatwa. Both wars fall within the wording of recent United Nations' Resolutions that address the adverse impact of terrorism on Human Rights. The understanding of the meaning of "terrorism" by those promoting the war on terrorism provides a powerful political tool, notwithstanding effects on Human Rights that are similar to the effects that result from the war of terrorism. These two wars signify a patterned break from the classical comity between nation-states with respect to acts of aggression, and the values being promoted in this context serve the emerging American Empire and the resistance to it. The result, framed by those promoting the war on terrorism, is that-either being for or against terrorism-potential for non-violent solutions are lessened. Since September 11, the war on terror has installed a new rule of preemptive self-defence, grounded in suspicion, and with no recent precedent in international law.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| 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".