Bibliographic record
Abstract
This book presents a theory of political disalignment and a revised theory of party realignment, using four case studies from the United States, Canada, Great Britain, and Italy to illustrate these concepts. Why do major political parties die? The shelf life of minor parties in democracies tends to be short, but major parties tend to be highly durable. The Democratic Party of the United States and the Conservative Party of the United Kingdom have been going strong for two centuries. Major parties perpetuate themselves by maintaining a consistent ideology on major national issues, even at the cost of periodic defeats at the polls. In American politics, ideological polarization maintains the vitality of the two major parties and renders them almost immune to threats from new parties, even as it impedes consensus and compromise on public issues. Spectacular instances of sudden death in major parties have nevertheless occurred in the United States, the United Kingdom, Canada, and Italy, and they all exhibit similar characteristics. The fatal event—which author Charles S. Mack calls "disalignment"—occurs when a schism opens between party leaders and traditional core-base voters on an issue of overriding national importance. Major parties survive periodic defeats, but they cannot survive disalignment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".