Bibliographic record
Abstract
My name is Rebecca Wilson and I live in Hyattsville, MD. I have served as an Election Judge and Chief Election Judge for 5 elections, including the General Election in 2004 and the General Election of 2006. The morning of the 2004 General Election produced 2-3 hour long lines. A huge number of voters tried to vote on their way to work. We had 3 of our 11 machines down when we opened and intermittent problems with 1 of them throughout the morning (the other 2 were paper jams on the zero tapes, which we were able to resolve). The backlog of voters didn't clear until about 11:00 am, so the people most affected were probably the ones who showed up between 7:30 and 8:30 am -- prime time for people en route to work. In the 2006 General Election, the thing that saved us from long lines for most of the day was the steady flow of people. But in the evening we had a big back-up of people after work. We locked the doors at 8pm and the last voter finished voting at 9:30 or 9:45, but the peak was probably about 6:30 - 7:30. We had 50 (if I remember correctly) Voter Access Cards, so we couldn't have more than a total of 50 voters actually at machines or in line at any time. We had a long line of people waiting to check in, but we couldn't check anyone in until a voter finished voting and returned the smart card. Once a voter checked in and had their card, they still had a fairly long wait for a machine to free up. Checking in using the E-pollbook takes about one minute. We had 2 E-pollbooks and 11touchscreen DREs that were taking voters as much as 45 minutes because the ballot was very long: The average time was probably from 15 to 25 minutes. There were 37 ballot items, including 19 election contests plus 4 state and 14 county ballot measures. The ratio of the number of DREs to the number of E-pollbooks was 5.5:1 whereas the ratio of the times for their use was more like 20:1. Thus the long lines were caused by the voting machines and not the E-pollbooks. I eased the situation as much as I could by going through the line and trying to weed out people who would have to vote provisionally, since there was no reason for them to wait in line. So anyone who had recently moved or wasn't sure if they were in the right polling place or had requested but not received an absentee ballot or whatever, I had the idle check-in judges look them up to see if they would need to vote provisionally. Those who did were able to get out of there much more quickly than those who were forced to wait in line for a machine.
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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 | 0.000 | 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".