Cross-Level Partisanship in Concurrent Federal-Provincial Elections:
Bibliographic record
Abstract
The purpose of this project is to explore the following research question: do same day (i.e. concurrent) provincial-federal elections exhibit a higher degree of cross-level partisanship than non-concurrent elections? This paper proposes that concurrent elections lead to a convergence in voters evaluations of federal-provincial co-partisans, and that this results in a higher degree of cross-level partisanship than in non-concurrent elections. Using 2011 Canada Election Studies (CES) data on federal party vote choice and provincial party preference, this paper will project the results of concurrent federal-provincial elections for three Canadian provinces. The results of these projected concurrent elections will be compared to actual party vote shares received in the first provincial election held following the 2011 Canadian federal election. The comparison of these data will be used to test the hypothesis that concurrent elections have a higher degree of cross-level partisanship than non-concurrent elections. This paper consists of five sections. First, I introduce the aims of this research and discuss its theoretical and substantive significance by referencing relevant literature. Second, a comprehensive theoretical framework is developed to explain why cross-level partisanship is expected to be higher in a concurrent election. Third, I outline the research design and methodology used to test this causal hypothesis. Fourth, I report and interpret my findings which show that overall cross-level partisanship was slightly higher in projected concurrent elections. I conclude by discussing the implications and limits of this study.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".