Countering foreign disinformation: Building a resilient Canadian democracy through stronger education and regulation
Bibliographic record
Abstract
Canada's Foreign Interference Commission released its initial report in May 2024, expressing concerns about meddling by foreign actors in Canadian elections and threats to public confidence in Canada's democratic institutions. Just three days later, the Canadian government tabled its response in the form of Bill C-70, the Countering Foreign Interference Act. Both constitute considerable progress and demonstrate Ottawa's willingness to act against the growing threat. However, both are limited—the report focusing on internal institutional dynamics, and Bill C-70 remaining mostly silent on one of the most important tactics of interfering countries: disinformation. Does Canada have the tools to respond to this threat effectively? In examining the Commission's reports, Bill C-70, and current Canadian practices, we argue that while Ottawa already deploys tactics to counter disinformation, it could do more by implementing two tested strategies: working with provincial, territorial and Indigenous governments to integrate media literacy within education curriculums, and implementing stronger regulation of social media platforms responsible for circulating 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".