Party Strategies and Voter Behavior in Multi-national States: Electoral Competition in a Multi-dimensional Issue Space
Bibliographic record
Abstract
This dissertation is about electoral competition in countries where the center-periphery dimension is salient alongside the traditional left-right dimension. The focus is on the strategic choices of ethno-regional parties in response to greater support for decentralization by their state-wide rivals. It focuses on 3 questions. 1) How do ethno-regional parties respond to their state -rivals’ efforts to co-opt their primary issue of regional autonomy? 2) Under what conditions and how much weight do voters attach to ethno-regional parties’ position on non-center-periphery issues? 3) What factors do ethno-regional parties have to consider when deciding which positions take on non-center-periphery issues. The data cover Western European countries and Canada over a period of close to thirty years, from the early 1990s to 2019. The dissertation tests the effect of greater support for regional autonomy by state-wide parties on the strategic behavior of ethno-regional parties and the response of voters to greater party convergence on the issue of regional autonomy. It then focuses on the Scottish National Party and the Bloc Quebecois. Qualitative data, panel data, and evidence from election studies are used to show that positioning on left-right ideological issues is crucial to the electoral fortunes of these ethno-regional parties. Finally, it uses simulations based on hypothetical party positions and voter preferences to explain the strategic constraints ethno-regional parties and their statewide rivals face when deciding where to position in a multidimensional issue space. The project advances our understanding of political behavior in multi-national states, electoral competition in multi-dimensional issue spaces, and the research on niche parties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".