Bibliographic record
Abstract
The October 2008 issue of PS published a symposium of presidential and congressional forecasts made in the summer leading up to the election. This article is an assessment of the accuracy of their models. The Time-for-Change Model proved one of the most accurate of the 2008 presidential election forecasts run in the October PS symposium. Using three predictors—the president's approval rating at mid-year, the growth rate of real GDP during the second quarter, and the time-for-change dummy variable—the model predicted that Barack Obama would win the presidential election with 54.3% of the major-party vote. According to nearly final tabulations compiled by uselections.org, as of December 8, Obama has received just over 53.6% of the major-party vote. However, it is likely that Obama's final total will reach 53.7% of the major-party vote. Therefore, the model's current error of 0.9 percentage points is likely to decrease further. The model has now correctly predicted the winner of the popular vote in all six presidential elections since its creation in 1988.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".