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. Prior to the 2008 presidential election we provided forecasts of the final vote relying on a model containing only two variables: (1) the cumulated weighted growth in leading economic indicators (LEI) through the thirteenth quarter of the sitting president's term; and (2) the incumbent party candidate's share in the most recent trial-heat polls. The novelty is the reliance on the advanced reading of the economy from the quarter ending in March of the election year. (The exact equation and the exact forecast change as the poll readings get closer to the election.) Our final forecast (Erikson and Wlezien 2008) based on trial-heat polls in August was that Barack Obama would win 52.2% of the two-party popular vote. This turned out to be quite close to the Election Day outcome of 53.5% (as of December 2), a little more than one percentage point above what we predicted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".