Bibliographic record
Abstract
The first part of this paper examines the history of wrongful convictions in terrorism cases with an emphasis on the Irish cases of the Birmingham Six, the Guildford Four, the Maguire Seven and Judith Ward. Various causes of wrongful convictions are examined including police misconduct, false confessions, ethnic stereotyping and lack of disclosure. The second part of the paper argues that miscarriages of justice that result in long term detention of the innocent in the post September 11 environment may be more likely to occur under immigration and military laws that offer far less procedural protections for detainees than the criminal law. Indeterminate detention under British immigration law, security certificates under Canadian immigration law and the rules for determining enemy combatant status under American military law are critically examined with an emphasis on the dangers of detaining the innocent. The third part of the paper concludes that a focus on wrongful criminal convictions is an inadequate approach to the problem of miscarriages of justice in the context of the contemporary war against terrorism. It proposes a definition of miscarriages of justice that stresses the risk of detaining those who are innocent under even the expanded liability rules of immigration and military law.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".