Bibliographic record
Abstract
This article examines how post-9/11 counterterrorism has often not been applied to far-right terrorism. Discriminatory double standards in counterterrorism should not be tolerated. Nevertheless, the answer is not simply to ratchet up counterterrorism to apply to the far-right. The legitimate desire for symbolic equality should not blind us to the underlying weaknesses of many of those instruments both in preventing terrorism and in respecting rights. In some cases, such as the United Nation’s regime of individual sanctions related to financing and travel of those associated with al Qaeda and Daesh, application to the far-right is not legally possible. It will be suggested in this article that far-right terrorism should be used as an opportunity to re-evaluate the effectiveness and propriety of all counterterrorism. A preliminary assessment suggests that counterterrorism tied to international or national proscription may not be effective (both generally and specifically in relation to the far-right). More difficult cases involve whether terrorism offences and offences targeting speech should be applied against all forms of terrorism. Interventions regulating items and material on the Internet used by terrorists and programs to counter violent extremism and to rehabilitate offenders may be promising in addressing both far-right and Daesh-inspired terrorism. Both New Zealand’s increased regulation of guns and the Christchurch calls for greater regulation of the Internet follow these more promising strategies.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".