Election administrators' perceptions of verifiable online voting and its use in local elections
Bibliographic record
Abstract
Canada is the longest user of online voting in municipal elections and has primarily used non-verifiable systems, raising concerns about the integrity of election results and public and administrator confidence in the process. In the 2022 Ontario municipal elections, 9% of municipalities offered online voters the option of individual verifiability. To better understand the considerations and challenges of introducing verifiability mechanisms in local elections, this article explores municipal administrators' perceptions and understanding of verifiable online voting through three focus groups with local governments in Ontario, Canada: (1) users of verifiable online voting systems,(2) users of non-verifiable systems, and (3) those without online voting. We find deeper reasonings for selecting non-verifiable online voting systems, such as administrators' perceptions of voters' needs and the perceived value of transparency. To enhance the adoption of verifiable online voting, the article suggests promoting the value and meaning of verifiability among all stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".