Contextualizing involvement in terrorist violence by considering non-significant findings: Using null results and temporal perspectives to better understand radicalization outcomes
Bibliographic record
Abstract
Irrespective of discipline, the publication of null or non-significant findings is rare in the social sciences. For burgeoning fields like terrorism research, this is particularly problematic. As well as increasing the likelihood of Type II errors, the selective reporting of significant findings ultimately impedes progression, hindering comprehensive syntheses of evidence and enabling ill-supported lines of scientific enquiry to persist. This manuscript discusses several structural and individual-level variables which failed to produce significant, linear associations with involvement in terrorist violence in a dataset (N = 206) of right-wing and jihadist extremists active in Europe and North America. After considering methodological factors such as non-random distributions of missing data, we illustrate how certain variables are significantly associated with involvement in terrorist violence at particular periods in a radicalizing individual's lifespan, but not others (i.e., pre- or post-radicalization onset). Moreover, we demonstrate that while some static, binary constructs (such as whether or not a radicalizing individual was exposed to diverse viewpoints) are not associated with terrorist violence, their influence over time produces different associations. We conclude that radicalization may be less about individuals having pre-disposing risk factors, such as biographical stressors, and more about cognitive changes that allow individuals to re-evaluate their lives through the lens of an extremist ideology. We also underline the importance of taking a temporal, rather than static, perspective to better understand the variables associated with the outcomes of radicalization trajectories.
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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 | 0.175 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".