The Trial-Heat Forecast of the 2008 Presidential Vote: Performance and Value Considerations in an Open-Seat Election
Why this work is in the frame
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Bibliographic record
Abstract
The trial-heat forecasting equation grew out of an examination of Gallup's trial-heat polls (“if the election were held today, who would you vote for?”) at various points in election years as predictors of the November vote (Campbell and Wink 1990). My co-author Ken Wink and I found, not surprisingly, that polls as literal forecasts were not very accurate until just before the election, that taking the historical relationship between the polls and votes into account through a bivariate regression significantly increased their accuracy, and that taking the contemporary context of the election as measured by economic growth in the election year into account increased their accuracy even further. Corroborating Lewis-Beck and Rice's earlier finding (Lewis-Beck 1985, 58), we found that an equation combining the Labor Day trial-heat poll standing of the in-party candidate and the second-quarter growth rate in the economy produced the most accurate forecast of the national two-party popular vote.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it