Shaping the game : federalism and voting behavior in advanced industrial democracies
Bibliographic record
Abstract
For countries that possess a federal structure, this institution is a crucial component of the constitutional arrangement of the nation. This institution arguably plays a role in nearly all aspects of a country's political environment. Although political science has much to say about federalism in the abstract, each country's federal system works differently. Unfortunately this variation has gone underappreciated in much of the scholarship on voting behavior. This dissertation seeks to inject our theoretical understanding of federalism, largely stemming from the works of William Riker, into comparative empirical analysis of voting behavior. As argued here, federalism in and of itself does not have a direct effect on behavior, instead it has indirect effects largely through the party system in place in a country. These theories will be tested for explaining differences in voter turnout cross-nationally and then again in more focused analyses of voter party choice in the three federal countries of the United States, Canada, and Germany. Ultimately the dissertation finds support for the theoretical and hypothesized effect of federalism on voters' decisions to turn out to vote, as well as how such allows for a considerable portion of the voting electorate in federal countries to cast inconsistent votes between the levels of government. These findings bring federalism back to the forefront of academic consideration in these types of studies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".