How to compare and analyse risks of internet voting versus other modes of voting
Bibliographic record
Abstract
Internet voting (I-voting) has been very topical and it has been used in elections in North America, Europe and elsewhere. The advantages of its use include increased participation from infirmed, elderly and itinerant voters and quicker vote tabulation. Its disadvantages stem from the inherent lack of control and transparency from allowing votes from homes and other locations rather than from controlled polling stations, which may lead to voter fraud or inadvertent spoilage of 'virtual' ballots. Much of this balancing-off of pros and cons is done on a qualitative basis with little attempt at quantifying the risks using an established methodology. The application of a well-known risk analysis method, Operationally Critical Threat, Asset and Vulnerability Evaluation (OCTAVE) has merely been proposed. In this paper, we extend this general proposal and describe key constructs for applying OCTAVE to perform risk analysis for voting alternatives in general. Governments or other institutions can then use this methodology to perform quantitative risk analysis to compare different voting alternatives including I-voting, as well as poll, mail-in and telephone voting.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".