Bibliographic record
Abstract
The question of who should judge the terrorists is an intriguing one This article seeks to understand why this is so by putting it into historical perspective. International law has a long history of dealing with terrorism, but was seemingly caught unprepared by the kind of nihilistic destructiveness implied by September 11. The challenge of 'hyperterrorism' can be seen as provoking a reorganization of the field. On the one hand, a brief cosmopolitan revival may be witnessed as several authors have urged the trial of major terrorists before an international criminal court. The argument, however, is unlikely to convince many and probably has more to do with liberalism's need to revitalize its programmatic promise in times that seem to profoundly challenge its globalizing logic. On the other hand is the notion, implemented in the United States, that terrorists should be judged by military commissions. This idea betrays a regression of international law and can only be properly understood if viewed in the larger context of a crisis of judicial liberalism. One intriguing element, however, is the way in which, beyond all the fuss generated by the international criminal court/military commissions debate, a great deal of what is wrong with the way that suspected terrorists have been dealt with has assumed decidedly more insidious forms.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.040 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".