Do Political Parties Pursue Similar Allocation Strategies?: Evidence from Unique Electoral Boundaries in Ontario
Bibliographic record
Abstract
The importance of spending money in elections and the limited resources available to political parties and candidates makes it reasonable to expect that parties strategically allocate resources to candidates (e.g., Schecter and Hedge 2001; Stonecash 1988; Pattie and Johnston 2009; Carty and Eagles 2005; Cross, 2004). While we find this literature to be quite informative, it is unclear how much local context affects the various strategies. Comparing party activity across levels of elections is challenging. One must worry about comparability of districts and other potential confounding factors. We expect that a significant advancement would be to employ a research design that controls the various constituency conditions across different types of elections in order to see whether similar political parties adopt the same strategy when facing the similar conditions. Using a real world situation from the province of Ontario, Canada, we evaluate party allocations when several provincial ridings (legislative districts) have the same boundaries as federal electoral districts. This boundary structure creates a rare opportunity for comparing party behavior in elections across levels of government while keeping most contextual factors the same (e.g., population). Our analysis focuses on party behavior during the 2003 provincial election and the 2004 federal election. Through this analysis, we determine that provincial and federal parties rarely adopt similar allocation strategies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".