Foreign Election Interference: Comparative Approaches to a Global Challenge
Bibliographic record
Abstract
Efforts by foreign entities to influence domestic elections have shaken democracies around the world. The use of propaganda and misinformation to interfere in the internal affairs of other countries is not new, but events since 2016 have heightened awareness across the globe of how changes in social media platforms, political norms, and campaign financing rules have enabled foreign actors to influence elections on an unprecedented scale. This special issue of the Election Law Journal explores how six nations have perceived and responded to this threat. These six nations—Canada, the United Kingdom, the Netherlands, Northern Ireland (as a constituent nation of the UK), Australia, and New Zealand—have faced their own challenges and forged ahead with their own solutions. As the contributions to this issue make clear, in doing so these nations have struggled with similar questions and have worked their way toward a common set of solutions. These efforts have varied in their details—which is what makes comparative review of them valuable—but they have consolidated around the same general set of ideas: better educating citizens about the perils of cyber speech, increasing transparency about who is promoting online communications, building better barriers to exclude foreign funding of electoral communications, and trying to remove the most egregiously false statements from political discourse. The hope of this research is that shedding light on how different nations have operationalized these efforts will demonstrate to election law scholars, regulators, and policy makers around the world the value of comparative work in this area. There is a great deal at stake, and much to learn from the experiences of others.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".