Surveillance, counter-terrorism and comparative constitutionalism
Bibliographic record
Abstract
"The decade after the September 11, 2001 terrorist attacks saw the enactment of anti-terrorism laws around the world that challenged understandings and assumptions about public institutions, human rights and constitutional law. Many of those laws remain on the statute books and continue to have a profound impact on constitutionalism and the rule of law. One of the most striking and rapid areas of development has been the conferral of increased powers of surveillance on law enforcement and intelligence agencies. The chapters in this edited book examine the impact of these powers on constitutionalism at both the domestic and international levels. The book discusses the prevalence of mechanisms of mass surveillance; the challenges that technological developments pose for constitutionalism; new actors in the surveillance state; the use of surveillance material as evidence in court and the difficulties of balancing secrecy and fair trial requirements; and the effectiveness of constitutional and other forms of review of surveillance powers. The contributors to the book who are leading international experts in anti-terrorism and constitutional law take a comparative approach looking at jurisdictions including the United States, Canada, United Kingdom, Europe, Israel, India, Japan, China and Australia. The book draws important conclusions about the constitutional implications, short- and long-term, domestic and international, of the expansion of surveillance powers after 9/11"..
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".