Introduction to A Research Agenda for Far-Right Violence and Extremism
Bibliographic record
Abstract
To identify key trends in the threat associated with far-right terrorism, it is critical to see the nuances in distinctive terrorism patterns. Rigorous and meticulous analyses of national patterns help us to better understand the broader causes and consequences of far-right terrorism. By delving into the most relevant circumstances, this trend and pattern analysis aims to better understand the nature of the threat posed by far-right terrorism. That is why this introductory chapter provides facts and figures on far-right terrorist attacks, modus operandi and perpetrators motivated by far-right extremism. Incidents which occurred between 2012 and 2022 in Canada, the United States, the United Kingdom, Norway, France, Germany, Australia and New Zealand are examined in the following analyses. Accordingly, data on the number of completed and thwarted attacks, the lethality of attacks, the diversity of perpetrators, used weapons and the targets of their operation are processed. Data for the trend and pattern analysis originates from the Global Terrorism Database, EUROPOL TESAT Reports, the ADL Center on Extremism, the University of Oslo’s Right-Wing Terrorism and Violence (RTV) dataset and open-source incident information. For consistency of the analyses, when defining far-right terrorism, it refers to the use of terrorist violence by far-right extremists. Far-right extremism embraces supremacist ideologies which feed on a variety of hateful sub-cultures. Racist behavior, authoritarianism, xenophobia, misogyny and hostility towards lesbian, gay, bisexual and transgender as well as immigrant communities characterize their ideologies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".