Protecting Electoral Integrity in the Digital Age: Developing E-Voting Regulations in Canada
Bibliographic record
Abstract
As elections around the world become digital, governments have begun adopting regulations to govern the use of voting technologies and protect electoral integrity. Canada, however, is an exception. Despite the prevalence of voting technologies in Canada's local elections, notably online voting, no regulation framework has been initiated. In particular, there are no guidelines or standards surrounding the use of online voting. While research documents online voting has positive effects for participation, implications for the integrity, accountability, and transparency of elections are stark. Canada's multilevel governance structure has meant municipalities mostly deliver elections on their own terms, resulting in a patchwork of online voting models and cybersecurity requirements. Many municipalities also lack the resources to vet vendor solutions adequately, and an increasing number of cities are eliminating paper voting. These conditions highlight an urgent need to regulate the design and procurement of election technology in Canada. To proactively respond to these developments, this article draws upon interviews with select officials and experts and regulation models in other jurisdictions to argue for a new model of electronic voting regulation that would be a good fit for Canada.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".