Scantegrity II: End-to-End Verifiability for Optical Scan Election Systems using Invisible Ink Confirmation Codes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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 it