Bibliographic record
Abstract
On one view, the main effect of a campaign is to enlighten voters about the means and ends dictated by “fundamentals” of competition in the current party system (Gelman and King, 1993; Zaller, 1998). The fundamentals assessed in this chapter are factors that endure across elections, indeed across decades. Some reflect party differences originating in the New Deal but reinforced by the policies of Lyndon Johnson's Great Society. Others reflect the “culture wars” of more recent decades. Importantly, enduring differences are also expressed geographically, in variation across states. This variation created, in turn, the possibility that the 2000 campaign would be a natural experiment on a continental scale. Identifying differences is only the starting point, however. For this book, fundamental factors are most interesting as they constrain, or fail to constrain, the dynamics of preferences over the campaign. Is the electorate indeed best characterized as a field of polarized interests, such that the campaign's primary effect is to increase preexisting gaps in vote intention, as citizens are reminded of the proper means to ends they hold dear? To the extent that this is so, shifts induced by the campaign should be mainly offsetting and the scope for the campaign to shape the result should, correspondingly, be small. The campaign would not be very interesting as a field for strategic play and counter play. Strategic initiatives may occur and, taken individually, may have their intended effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.016 |
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".