Fourth International Joint Conference on Electronic Voting, E-Vote-ID 2019 : 1-4 October 2019, Lochau/Bregenz, Austria : Proceedings
Bibliographic record
Abstract
This volume contains papers presented at E-Vote-ID 2019, the Fourth International Joint Conference on Electronic Voting, held during October 1-4, 2019, in Bregenz, Austria.It resulted from the merging of EVOTE and Vote-ID and counting up to 15 years since the first E-Vote conference in Austria.Since the first conference in 2004, over 1000 experts have attended the venue, including scholars, practitioners, authorities, electoral managers, vendors and PhD Students.The conference collected the most relevant debates on the development of Electronic Voting, from aspects relating to security and usability through to practical experiences and applications of voting systems, also including legal, social or political aspects, amongst others; turning out to be an important global referent in relation to this issue.Also, this year, the conference consisted of:-Security, Usability and Technical Issues Track -Administrative, Legal, Political and Social Issues Track -Election and Practical Experiences Track -PhD Colloquium, Poster and Demo Session on the day before the conference E-VOTE-ID 2019 received 45 submissions, being, each of them, reviewed by 3 to 5 program committee members, using a double blind-review process.As a result, 23 papers were accepted for this volume, representing 51% of the submitted proposals.The selected papers cover a wide range of topics connected with electronic voting, including experiences and revisions of the real uses of E-voting systems and corresponding processes in elections.We would also like to thank the German Informatics Society (Gesellschaft für Informatik) with its ECOM working group and KASTEL for their partnership over many years.Further we would like to thank the Swiss Federal Chancellery for their kind support.Special thanks go to the members of the international program committee for their hard work in reviewing, discussing, and shepherding papers.They ensured the high quality of these proceedings with their knowledge and experience.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.125 | 0.063 |
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".