Routledge handbook of law and terrorism
Bibliographic record
Abstract
"In the years since 9/11 counter-terrorism law and policy has proliferated across the world. This book sets out a comprehensive survey of how the law has been deployed in all aspects of counter-terrorism. The handbook provides an authoritative and critical analysis of how laws are, and ought to be, invoked in domestic jurisdictions against terrorism. With a comparative approach the focus is on those jurisdictions which have produced legal innovations with a sizeable impact, primarily the USA, the UK, Australia, Canada, France, Germany and the European Union. The never before published contributions to the book are written by experts in the field of terrorism law and policy, allowing for discussion of a wide range of regulatory responses and strategies of governance. The book is divided into four parts: the boundaries and strategies of national counter-terrorism laws; the pursuit of terrorists through national criminal process and executive measures; protective security; and preventive measures. The chapters engage with areas of traditional interest to lawyers such as policing and special powers, criminal offences and the courts, and prison regimes but also tackle emerging subjects including preventing radicalisation and protective/preparative security. In this way the handbook reflects the elements of counter-terrorism laws which are more transformative of mass movements and transactions alongside prosecutions or orders aimed at particular individuals"..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.126 | 0.067 |
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".