Turnout and Voting Methods in the United States - How Shiny is the Shining City?
Bibliographic record
Abstract
This presentation combines and applies principles from both political science and geographic information science in order to gain unique insights into electoral politics and voter turnout in the United States. Missouri is used as a case-study because it has a longstanding history as a political bellwether due to its unique position straddling the historic North/South and the contemporary East/West divide; in addition its socioeconomic background closely mirrors national averages. Since the United States leaves the method of voting a responsibility of the counties, there is a discrepancy in the types of technologies used. This can result in some electorates being more prone to faulty machinery, which in turn leads to errors at the ballot box. In order to effectively answer the question- what was the effect that varying voting methods had on the turnout in Missouri during the 2004 Presidential election? - a multi-disciplinary approach must be taken which applies both a qualitative background to provide context and a strong quantitative element to provide hard empirical results. In order to address the quantitative element, due to the spatial nature of the data being used, regular statistical analysis is not possible without inviting inherent errors. Instead a variety of spatial statistics will be used to help address the hypothesis that counties which have lower voter turnouts will have less reliable methods of voting.
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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 | 0.022 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".