A matter of quality?: candidates in Canadian consituency elections
Bibliographic record
Abstract
Most students of Canadian elections emphasize the role of national campaigns in deciding the outcome of these elections. I challenge this orthodoxy as incomplete and argue that we need to pay more attention to who runs for Parliament. Without studying what happens in the constituencies and among the individuals who seek to become parliamentarians, we cannot fully understand the process of Canadian electoral politics. For that reason, I shine some light on the hundreds of individuals who run for Canada's Parliament during each general election. In doing so, I seek to identify the ways in which candidates might matter in Canada and then test a number of hypotheses related to their effect on Canadian elections. This study is the first empirical examination of the role candidates play in Canadian elections. I classify candidates as being either quality or non-quality based on their previous political experience and occupation. The American political science literature suggests that quality candidates are better candidates. They raise and spend more money; they attract more volunteers; and, they are more likely to win their elections. Testing this American concept on the 2004, 2006, and 2008 Canadian General Elections, I find that American findings hold true in Canada. Quality candidates, in contrast to non-quality candidates, are more strategic and nm when conditions are favourable. They also raise more money and perform better on Election Day, all else being equal.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".