Facts, Fallacies, and Frames: Exploring the Nexus Between Political Decision-Making and Counterterrorism Resource Allocation
Bibliographic record
Abstract
This dissertation is about political decision-making and counterterrorism resource allocation.Why have Canada and the United States allocated more resources to countering Islamist-inspired terrorism than right-wing extremism, even though the latter has committed more attacks and caused more fatalities since September 11, 2001?Why do political leaders prioritize some threats over others?What is the role of the public service in decision-making?And how do various actors within the national security apparatusespecially those of different generationsinterpret the current threat landscape?To answer these questions, this dissertation challenges the rational-utility model and tests three counter explanations: (1) political self-interest, (2) path dependence/bureaucratic inertia, and (3) framing theory.Using a sample of 35 lethal violent extremism attacks between 1995 and 2021, hundreds of open-sourced texts, a variety of quantitative methods, and 30 original interviews conducted with current and former public servants who work(ed) in the Canadian and US intelligence and security (I&S) community, the results suggest that the answer is multicausal.In addition to the tested theories, political leaders rely heavily on party considerations, high fatality events, symbolic attacks, international threat considerations, and mass and social media influences.The findings also affirm that public servants play an important role in shaping the policy agenda and that deep, albeit shrinking, cleavages persist between the various departments and agencies working in the I&S community.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".