Bibliographic record
Abstract
What is the strength of incumbency advantage in a non-partisan electoral system? More specifically, does incumbency advantage in non-partisan electoral systems align with or contradict what we expect in a partisan electoral system? Little to no work has examined whether the conclusions of the standard incumbency advantage literature travel to non-partisan systems. This thesis uses Canada’s three Northern territories as a case study to compare incumbency advantage partisan versus non-partisan political systems. The thesis uses a regression discontinuity design and logistic regression models to measure incumbency advantage in the territories. The thesis finds mixed results on incumbency advantage. On one hand, the results suggest that there a weak scare-off effect experienced by incumbents in territorial elections. Moreover, contrary to expectations, incumbency is weaker in rural districts compared to urban districts. Lastly, the findings of this thesis suggest that incumbency advantage in Nunavut is weaker than in the Northwest Territories. With that being said, no difference in incumbency was found between in consensus government and the Yukon’s partisan system. This thesis contributes to three subfields of political science research. First, this thesis demonstrated the methodological challenges of working with small samples sizes, which have the tendency to limit the ability to use causal inference methods. Likewise, this thesis contributed to the incumbency advantage literature, using a non-partisan case study, which showed that partisanship should not be over-emphasized when studying incumbency. Lastly, this paper contributed to the Northern Canadian politics literature, by presenting a new dataset on territorial elections, and using quantitative methods on a case study that has previously relied on qualitative research
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".