Bibliographic record
Abstract
Terrorism trials are an exceptional opportunity for better understanding and, hence, countering terrorism, since they are often the only place where most if not all of the actors of a terrorist incident meet again, and where the media report and broadcast their respective accounts. Seeing terrorism trials as a stage where legal instruments are used (and abused) to argue the validity of contested political constructs, this study presents a performative perspective to draw attention to the mechanisms and effects of terrorism trials in and outside the courtroom. With a special focus on how the power of these performances may in turn shape new narratives of justice and/or injustice, it offers vital insights into terrorism trials directed involving different types of terrorism suspects, from left-wing to ethno-nationalist and jihadist terrorists, in Spain, Russia, Germany, the Netherlands, and the US.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".