Limping into the Future: The U.N. 1267 Terrorism Listing Process at the Crossroads
Bibliographic record
Abstract
UN listing of al Qaeda and Taliban affiliates under the Security Council resolution 1267 system has been controversial, in large measure because of the absence of due process and the secrecy surrounding the decisions made by the sanctioning committee. There have been a number of successful domestic challenges to the implementation of this system at the national and supranational level. If domestic and supranational courts continue to invalidate domestic implementation of 1267 listings, there will be a disconnect between the global 1267 list and certain domestic lists. The 1267 process may be able to survive some domestic challenges and exemptions, but criticisms by domestic judges will erode support for the 1267 system. This may not in itself be a bad development, as 1267 listing, with its focus on al Qaeda and the Taliban, is only a partial response to international terrorism. Even apart from the human rights implications of listing, it is not clear that listing and related terrorism financing and travel ban interventions are particularly effective means to combat today’s decentralized and often homegrown terrorism. Listing may be an example of fighting the last war against al Qaeda rather than deploying tools to forestall the next form of terrorism.This Article explores these issues in four parts. In Part I, we provide a brief overview of the 1267 system and its origins and operations. In Part II, we examine the substantive international law that may apply to the Security Council as well as the jurisdictional basis for (and possible constraints on) the 1267 system. The focus here is on the possibility of applying due process protections derived from various forms of international law to the 1267 listing process. In Part III, we examine some of the “dualist” defenses of due process, examining domestic and supranational court decisions in the European Union, the United States, and Canada. In Part IV, we discuss some of the lessons that can be drawn from both international and domestic attempts to increase the fairness of terrorist listing processes. This includes the common and difficult challenge in both the international and domestic realms of providing for adversarial challenges to secret intelligence that is said to justify listing. Finally, we reflect on the implications of these challenges to the sustainability of listing processes, terrorism financing, and judicial review of counterterrorism actions in general.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".