Combatting Foreign Election Interference: Canada's Electoral Ecosystem Approach to Disinformation and Cyber Threats
Bibliographic record
Abstract
Foreign election interference presents a significant threat to electoral fairness, democratic legitimacy, and public confidence in elections. This article argues for an “electoral ecosystem” approach to combatting the threat of foreign intervention in elections. Under an electoral ecosystem approach, the electoral system is viewed as an interconnected network of institutions, processes, and actors, all of which must coordinate together to ensure electoral effectiveness and legitimacy. An electoral ecosystem is comprised of multiple institutions and actors, including governments, political parties, voters, third parties, online platforms, and electoral management bodies. Given the interdependence and interconnected nature of an electoral system, there are multiple points of vulnerability that must be defended. An electoral ecosystem approach does not depend on any one single line of defense but instead relies on a multiplicity of strategies that protect the institutions and individuals that comprise the ecosystem. To further explore the electoral ecosystem approach, this article focuses on Canada's response to foreign interference in elections. The ecosystem approach consists of three principal strategies. The first strategy involves a set of new campaign finance regulations which directly prevents the influence of foreign individuals and groups on the election. The second strategy consists of new measures to reduce disinformation and to lessen its distorting impact on democratic discourse. To the extent that foreign election interference takes place through social media, efforts to reduce disinformation will also reduce the impact of foreign interference. The third strategy involves strengthening Canadian cybersecurity. A number of steps have been taken recently, including the coordination of Canada's security agencies and the adoption of new measures to prevent computer hacking and privacy intrusions. These three strategies, and the multiple measures within each, provide protection across the electoral ecosystem.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".