Simple Method for Predicting American Presidential Greatness From Victory Margin in Popular Vote (1824-1996)
Bibliographic record
Abstract
The author tested the simple method (SM) for predicting presidential greatness from the winner's victory margin in the popular vote and A. M. Schlesinger Jr.'s (1986) cycles of American political history with the expert sample presidential rankings of W. J. Ridings Jr. and S. B. McIver (1997). The SM, which involves only simple calculations on minimal data available shortly after an election, predicts greatness ratings that are above average for winners with high victory margins in years of public purpose and for winners with low victory margins in years of private interest. Also, the SM predicts ratings that are below average for winners with low victory margins in public purpose years and for winners with high victory margins in private interest years. Based on the data for 42 elections from 1824 to 1996, the SM success rate was 81.0% for all elections, 85.2% for the 27 1st-term elections, 86.2% for elections after 1880, and 94.4% for 1st-term elections after 1880. Chi-square analyses showed all percentages significant at the .001 level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".