Countering lone-actor terrorism: specification of requirementsfor potential interventions
Bibliographic record
Abstract
The author presents the de-classified preliminary findings of the European Commission funded FP7 research project PRIME, dealing with the extremism, radicalization and lone-actor terrorism (also known as “lone wolf terrorism”). The Article provides the partial results of the research devoted to the preparation of portfolio of lone actor extremism counter-measures requirements based on the findings of the review of existing counter-measures used to defend against lone actor extremist events. The Article concludes with a list of recommendations, which shall be considered in order to prevent, interdict and mitigate the threat of lone actor extremism and terrorism and to support public security and safety. These recommendations are based on the extensive consultations with law-enforcement and security services practitioners and Subject Matter Experts of the PRIME Project domain, representing a wide range of areas (police, intelligence, border protection, military, government, civil defence, non-governmental organizations, and the academic community) and different jurisdictions and law practices (several countries of Europe, United States, Canada, India, Japan, Georgia, Mexico, Australia and New Zealand).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".