Dual accountability and the nationalization of party competition: Evidence from four federations
Bibliographic record
Abstract
This paper assesses the extent to which party systems are nationalized in four federations. In doing so, the research addresses two questions. First, is dual accountability operational across decentralized countries, or do sub-national voters turn to national cues as a means to economize in a complex information environment? By bringing a cross-national dataset to bear on this question, we are able to provide insight into where and why dual accountability might operate. Second, what explains variation in the extent to which party systems are nationalized across countries and time? We build on previous literature to suggest a number of factors likely to impact the extent of nationalization. We examine those factors in the context of provincial-level elections in Argentina, Canada, Germany and the United States. Using national and sub-national economic data, we find little evidence of dual accountability in any of our countries. We find that economic performance matters little for regional electoral outcomes, and where it does, sub-national outcomes reflect national rather than sub-national conditions. More important are the roles of partisan relations across levels of government and election timing. Sub-national co-partisans of the nationally governing party lose votes, particularly as the time from the most recent national election grows. The strength of these effects varies across our cases in predictable ways.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".