The Proscription of Terrorist Organisations:Modern Blacklisting in Global Perspective
Bibliographic record
Abstract
Powers to outlaw or proscribe terrorist organisations have become cornerstones of global counter-terrorism regimes. In this comprehensive volume, an international group of leading scholars reflect on the array of proscription regimes found around the world, using a range of methodological, theoretical and disciplinary perspectives from Political Science, International Relations, Law, Sociology and Criminology. These perspectives consider how domestic political and legal institutions intersect with and transform the use of proscription in countering terrorism and beyond. The chapters advance a range of critical perspectives on proscription laws, processes and outcomes, drawing from a global range of cases including Australia, Canada, the EU, Spain, Sri Lanka, Turkey, the UK and the USA. Using single and comparative cases, the authors emphasise the impacts of proscription on freedoms of speech and association, dissent, political action and reconciliation. The chapters demonstrate the manifold consequences for diasporas and minorities, especially those communities linked to struggles overseas against oppressive regimes, and stress the significance of language and other symbolic practices in the justification and extension of proscription powers. The volume concludes with an in-depth interview on the blacklisting of terror groups with the former U.S. Director of National Intelligence, James Clapper. This book was originally published as a special issue of the journal Terrorism and Political Violence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".