C, B, R, or N: The Influence of Related Industry on Terrorists' Choice in Unconventional Weapons
Bibliographic record
Abstract
This study explores which factors, given that a terrorist has crossed the threshold into using chemical, biological, radiological, and nuclear (CBRN) over conventional weapons, will determine the likelihood that he/she chooses to use C, B, R, or N weapons. Relying primarily on data from the incident-based Monterey WMD Database, it employs multinomial logit regression with C, B, R, or N as a categorical dependent variable: a first within the relevant econometric literature. Fundamentally, the study tests the widely-held—although empirically unsubstantiated—technological deterministic assumption that the more readily CBRN technology, materials, and knowledge are accessible to terrorists, the more likely terrorists will be to use unconventional weapons of the corresponding kind: a relationship hypothesized to be stronger for serious attack perpetrators than for hoaxers. Next, the study tests the notion of a continuum of proliferation potential, hypothesizing that as states’ regulatory capacity increases, biological terrorism will be most likely and nuclear terrorism will be least likely. Finally, the study assesses variables that have previously been proven as significant determinants of CBRN over conventional terrorism, to provide the groundwork for future evaluation of the extent to which terrorists may be induced to pursue C, B, R, or N over conventional weapons.Cette étude explore les facteurs, en supposant qu'un terroriste ait franchi le seuil des armes traditionnelles en utilisant des armes non traditionnelles comme les armes chimiques, biologiques, radiologiques et nucléaires (CBRN), qui détermineront la possibilité qu'il/elle choisisse d'utiliser les armes C, B, R ou N. En s'appuyant sur des données primaires de labase de données Monterey WMD, elle se sert d'une régression logit multinomial avec C, B,R ou N comme une variante dépendante catégorique : il s'agit d'une première pour la documentation pertinente économétrique. Principalement, l'étude vérifie la supposition très répandue - même si elle n'est pas empiriquement corroborée - comme quoi plus latechnologie CBRN, les matériaux et les connaissances sont accessibles aux terroristes, plus les terroristes seront portés à se servir d'armes non traditionnelles pour la situation quis'apprête : une relation censée être plus solide pour des auteurs d'agressions plus dangereuses que pour les charlatans. Ensuite, l'étude vérifie la notion d'une continuité de prolifération éventuelle, en supposant qu'avec l'augmentation de la capacité deréglementation de l'État, le terrorisme biologique serait le plus probable, alors que le terrorisme nucléaire serait le moins probable. Enfin, l'étude évalue les variantes qui ont déjà été prouvées comme déterminant de façon importante les CBRN plutôt que le terrorisme traditionnel, pour fournir un travail préparatoire pour de futures évaluations de l'étendue avec laquelle les terroristes pourraient être incités à se servir d'armes C, B, Rou N plutôt que des armes traditionnelles.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".