Fake Terrorism: Examining terrorist groups’ resort to hoaxing as a mode of attack
Bibliographic record
Abstract
Little academic attention has been accorded to terrorism hoaxers-i.e.those perpetrators who use lies, benign materials and/or empty threats to give the impression that a terrorist act is or has been underway.This dissertation harnesses under-utilised terrorism events data to build a theory of hoaxes in pursuit of a dual aim: to provide a robust substantive answer to the empirical puzzle of why hoaxes are used, but not by all groups, and not all the time; and to evaluate the degree to which existing data can demystify the hoax phenomenon.The starting point is a rationalist framework for terrorist groups' strategic logics, which emphasizes the relative costs and benefits of hoaxes in relation to serious terrorism activity.In the empirical theory-building chapters, probit regression and qualitative comparative analysis (QCA) are used to identify various organizational conditions that differentiate hoaxers from non-hoaxers, thereby indicating which strategic logics are plausibly at play, and in which contexts.A statistical cluster analysis demonstrates that there are five broad classes of hoaxing terrorist groups, which differ from one another along motivational, structural, and campaign contextual lines.While the unit of analysis throughout is the terrorist group, these analyses rely on crossnational terrorism events databases-predominantly ITERATE and the Monterey WMD Terrorism Database-to identify which groups never hoax, and which groups sometimes do.In the dissertation's final section, earlier findings are tested against a new sample of terrorism perpetrators derived from the recently-released Canadian Incident Database (CIDB).Although the Canada-centric data reveals a biased under-reporting of hoax activity in the cross-national datasets, a QCA analysis of its perpetrators reveals roughly similar conditions differentiating hoaxers from non-hoaxers.The CIDB's comprehensive events coverage is further exploited to test whether these organizational indicators and their associated hypothesized mechanisms hold, when campaign activities are evaluated at the event-level.A fine-grained analysis of event sequencing in Canada's most prolific terrorism campaign (that of the Front de libération du Québec) corroborates a range of proposed strategic logics.The observational nature of available data is thus limited in its ability to clarify hoaxers' strategic logics, which are both over-determined and equifinal.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".