Significance Quest: A Meta-Analysis on the Association Between the Variables of the 3N Model and Violent Extremism
Bibliographic record
Abstract
Given the pervasiveness of violent extremism all over the globe, understanding its psychological underpinnings is key in the fight against it. According to the Significance Quest Theory and its 3N model, violent extremism (i.e., violent and deviant behavior) is a function of three elements: need, narrative, and network. In the present meta-analysis, to put into test the theory and its model, we aimed to establish the strength of the association between these three elements, as well as the quest for significance itself, and violent extremism; and investigate if these associations are influenced by methodological decisions (i.e., sampling and measurements/manipulations). A literature search was performed through electronic platforms, a call for unpublished or in-press data, and backward snowballing. Seventeen reports, comprising 42 studies, met full inclusion criteria: quantitative studies based on primary data assessing for the association of at least one of the 3Ns, or quest for significance, and violent extremism, and providing sufficient data for effect size extraction. Findings are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines. Random-effect meta-analyses rendered statistically significant pooled effect sizes in all the investigated associations. The association is strong for quest for significance, moderate for narrative and network, and low for need for significance. Subgroup analyses demonstrate that the detection of these associations is influenced by methodological decisions concerning the measurements and manipulations, but not by those concerning the sampling. We discuss these findings and suggest future research directions aiming to improve the predictive power of the theory and its model.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".