The Trial-Heat Forecast of the 2008 Presidential Vote: Performance and Value Considerations in an Open-Seat Election
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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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".