Distinguishing lone from group actor terrorists: A comparison of attitudes, ideologies, motivations, and risks
Bibliographic record
Abstract
The increasing recognition of the risks posed by lone-actor terrorists provides the impetus for understanding the psychosocial and ideological characteristics that distinguish lone from group actors. This study examines differences between lone and group actor terrorists in two domains: (i) attitudes toward terrorism, ideology, and motivation for terrorist acts; and (ii) empirically derived risk factors for terrorism. Using a cross-sectional research design and primary source data from 160 men convicted of terrorism in Iraq, this study applied bivariate and logistic regression analyses to assess group differences. It tested the hypothesis that there are no statistically significant differences between the groups. Bivariate analyses revealed that lone actors were less likely than group actors, to be unemployed, to cite personal or group benefit as the main motives for terrorist activity, and to believe that acts of terrorism achieved their goals. Regression analysis indicated that having an authoritarian father was the only factor that significantly predicted group membership, with group actors three times more likely to report this trait. Lone actors and group actors are almost indistinguishable except for certain differences in attitudes, motives, employment, and having an authoritarian father.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".