Covert Derogations and Judicial Deference: Redefining Liberty and Due Process Rights in Counterterrorism Law and Beyond
Bibliographic record
Abstract
This article considers the use of control orders in the United Kingdom as an example of one of the most important legal aspects of the “war on terror”: the development, alongside the criminal justice approach, of a pre-emptive system. It argues that in relation to such orders the executive has in effect sought to redefine key human rights in a manner that, at its most extreme, amounts to covert derogation, and that both Parliament and the judiciary have been to an extent drawn into and made complicit in this process. It highlights key aspects of this story in order to illustrate some broader points about the role of judges, Parliament, and the rule of law in response to such exceptional measures. It argues that the attempted minimization of the ambit of rights, the spreading use of secret evidence, and the damaging constitutional impact of excessive judicial deference, are of great significance beyond UK counterterrorism law and can help illuminate both the opportunities and the dangers in constitutional dialogue.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 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".