Dynamics of the 2000 Republican Primaries
Bibliographic record
Abstract
The 2000 presidential primaries were among the liveliest in recent memory. This article is the authors' first account of the changing fortunes of the candidates from the Iowa caucuses through Super Tuesday. It is based upon the nomination phase of the Annenberg 2000 Election Surveys, a collection of nearly 32,000 interviews conducted from November through March, nationwide and in special-purpose state and regional studies, on a broad range of political science and communications questions. The analysis of dynamics is facilitated by the survey's rolling cross-section design, in which the day of interview is itself a product of random selection. This account emphasizes the interplay between substantive and strategic contributions to the votes cast at different points in the campaign, between evaluations of the candidates as people and policymakers, on the one hand, and judgments about the candidates' chances of winning a party's nomination and the general election, on the other. The pervasive influence of information is demonstrated. The knowledge voters managed to acquire through the campaign informed both kinds of considerations. The weight voters gave such considerations depends on the store of information they managed to accumulate about the candidates.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".