Bibliographic record
Abstract
Abstract All federal systems face an internal tension between divisive and integrative political forces, striking a balance between providing local autonomy and representation on one hand and maintaining an integrated political community on the other hand. How multi-level systems strike this balance depends on the development of styles of either integrated politics, which creates a shared framework for political competition across the units of a federation, or independent politics, preserving highly autonomous arenas of political life. This book argues that the long-term development of integrated or independent styles of politics in multi-level systems can be shaped by two key elements of federal institutional design: the degree of fiscal decentralization, or how much is ‘at stake’ at each level of government, and the degree to which the allocation of policy jurisdiction creates legislative or administrative interdependence or autonomy. These elements of federal institutional design shape integrated and independent politics at the level of party organizations, party systems, and voter behaviour. This book tests these arguments using a mixed-method approach, drawing on original survey data from 250 subnational party leaders and aggregate electoral data from over 2,200 subnational elections in seven multi-level systems: Canada, the United States, Australia, Austria, Germany, Switzerland, and Spain. It supplements this with configurational analysis and qualitative case 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".