MétaCan
Menu
Back to cohort
Record W3121666301 · doi:10.1109/eee.2005.147

Who Counts Your Votes?

2005· article· en· W3121666301 on OpenAlexaffabout
Halina Kaminski, Lila Kari, Mark Perry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsWestern University
Fundersnot available
KeywordsVerifiable secret sharingCorrectnessElectronic votingVotingComputer scienceComputer securityProtocol (science)DemocracyProcess (computing)Internet privacyPolitical scienceLawAlgorithmOperating systemProgramming language

Abstract

fetched live from OpenAlex

Open and fair elections are paramount to modern democracy. Although some people claim that the pencil-and-paper systems used in countries such as Canada and UK are still the best method of avoiding vote rigging, recent election problems have sparked great interest in managing the election process through the use of electronic voting systems. It is a goal of this paper to describe a voting system that is secret and secure as well as verifiable and useable over an existing computer network. We have designed and implemented an electronic voting system - verifiable e-voting (VEV) - with an underlying protocol that secures the election process from malicious practices at the same time as allowing voters and candidates to verify the correctness of their votes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.016
GPT teacher head0.248
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2005
Admission routes2
Has abstractyes

Explore more

Same topicInternet Traffic Analysis and Secure E-votingFrench-language works237,207