Bibliographic record
Abstract
voting software available for use in public elections to begin a process founders hope will transform the voting system from a fraud-prone, blackbox, proprietary, expensive, idiosyncratic, unreliable system to a technically sound, accurate, secure, inexpensive, uniform and open voting system. An international team of volunteer scientists and engineers developed the demonstration system. Jan Kärrman of Sweden, a senior research engineer at Uppsala University says that the role of the U.S. internationally “makes it important, outside the U.S. as well, that fair elections are being held there. " John-Paul Gignac of Canada wrote the software for the graphical user interface. Anand Pillai of Bangalore India, Eron Lloyd of Pennsylvania, and Dr. David Mertz of Massachusetts have been the other main software code contributors. Fred McLain, a noted computer security expert from Washington, has served as the lead developer over the past two months. “I am very pleased with the outstanding contributions of this world wide group of contributers. In a short period of time they have created a ballot system with a paper trail, an outstanding verification system and allow for vision impaired users as well, ” McLain stated. MORE…. A simulation of the poll-site voting machine is available on the Internet. Users can print the
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 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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".