Stress Tested: The COVID-19 Pandemic and Canadian National Security
Bibliographic record
Abstract
Leading experts analyze the impacts of the global COVID-19 pandemic on Canada’s national security. \n \nThe emergence of COVID-19 has raised urgent and important questions about the role of Canadian intelligence and national security within a global health crisis. Some argue that the effects of COVID-19 on Canada represent an intelligence failure, or a failure of early warning. Others argue that the role of intelligence and national security in matters of health is—and should remain—limited. At the same time, traditional security threats have rapidly evolved, themselves impacted and influenced by the global pandemic. \n \nStress Tested brings together leading experts to examine the role of Canada’s national security and intelligence community in anticipating, responding to, and managing a global public welfare emergency. This interdisciplinary collection offers a clear-eyed view of successes, failures, and lessons learned in Canada’s pandemic response. \n \nAddressing topics including supply chain disruptions, infrastructure security, the ethics of surveillance within the context of pandemic response, the threats and potential threats of digital misinformation and fringe beliefs, and the challenges of maintaining security and intelligence operations during an ongoing pandemic, Stress Tested is essential reading for anyone interested in the lasting impacts of the COVID-19 pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".