Scantegrity II: End-to-End Verifiability for Optical Scan Election Systems using Invisible Ink Confirmation Codes
Bibliographic record
Abstract
We introduce Scantegrity II, a practical enhancement for optical scan voting systems that achieves increased election integrity through the novel use of confirmation codes printed on ballots in invisible ink. Voters mark ballots just as in conventional optical scan but using a special pen that develops the invisible ink. Verifiability of election integrity is end-to-end, allowing voters to check that their votes are correctly included (without revealing their votes) and allowing anyone to check that the tally is computed correctly from the included votes. Unlike in the original Scantegrity, dispute resolution neither relies on paper chits nor requires election officials to recover particular ballot forms. Scantegrity II works with either precinct-based or central scan systems. The basic system has been implemented in open-source Java with off-the-shelf printing equipment and has been tested in a small election. An enhancement to Scantegrity II keeps ballot identification and other unique information that is revealed to the voter in the booth from being learned by persons other than the voter. This modification achieves privacy that is essentially equivalent to that of ordinary paper ballot systems, allowing manual counting and recounting of ballots.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".