Campaigns of Disinformation: Modern Warfare, Electoral Interference, and Canada’s Security Environment
Bibliographic record
Abstract
This capstone develops a risk assessment of Canada’s democratic institutions as it relates to potential foreign interference in the 2019 federal election, determining what factors should be of most concern for Canadian policymakers charged with defending Canada’s democratic infrastructure from foreign interference. Through a literature review of non-linear warfare, social media networks and algorithms, I discuss the technological and behavioural factors that have contributed to the vulnerabilities in Western states, namely the advent of a changing media and information environment. I argue that changes in technological and information accessibility have allowed states who have been traditionally disadvantaged in terms of warfare capabilities to overcome these asymmetries. I go on to explore cases of recent interferences in domestic by Russia and China, providing a comprehensive overview of each state’s motivations for engaging in non-linear warfare: historical grievances against the Liberal International Order, changing tactics in modern warfare, and interference strategies in foreign elections. Following these case studies, I then assess why Canada is viewed as a target from the perspectives of both Russia and China, and the different motivations of these states in intervening in Canada. Following an assessment of Canada’s security environment as it relates to election interference, I evaluate the safeguards Canada has put in place to defend against attacks to its democratic institutions. I devise a risk matrix that codifies the threats Canada will likely face during the pre-election and election period leading up to the 2019 federal election. This capstone concludes with a recommendation that urges Canada to institute a National Centre for Strategic Communications and Digital Democracy that would develop cohesive strategies to safeguard democracy in Canada against foreign influence campaigns, cyber-attacks, and disinformation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".