A transparent referendum protocol with immutable proceedings and\n verifiable outcome for trustless networks
Bibliographic record
Abstract
High voter turnout in elections and referendums is very desirable in order to\nensure a robust democracy. Secure electronic voting is a vision for the future\nof elections and referendums. Such a system can counteract factors that hinder\nstrong voter turnout such as the requirement of physical presence during\nlimited hours at polling stations. However, this vision brings transparency and\nconfidentiality requirements that render the design of such solutions\nchallenging. Specifically, the counting must be implemented in a reproducible\nway and the ballots of individual voters must remain concealed. In this paper,\nwe propose and evaluate a referendum protocol that ensures transparency,\nconfidentiality, and integrity, in trustless networks. The protocol is built by\ncombining Secure Multi-Party Computation (SMPC) and Distributed Ledger or\nBlockchain technology. The persistence and immutability of the protocol\ncommunication allows verifiability of the referendum outcome on the client\nside. Voters therefore do not need to trust in third parties. We provide a\nformal description and conduct a thorough security evaluation of our proposal.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".