Assessing the impact of issue ownership voting in the rest of Canada (ROC) during the 2015 Canadian federal election
Bibliographic record
Abstract
With the effects of issue ownership voting being assessed within previous Canadian elections, this thesis analyzes how issue ownership theory serves as a predictor of individual vote choice in the 2015 Canadian federal election. A binary logistic regression is used to examine how issue ownership theory emerges across each psychological dimension (associative or competency) and on a variety of issues – including the economy, health care, education, environment, crime and defense – to ultimately predict vote choice for each major political party (Liberal, Conservative, and NDP). Our results show a relationship between both the associative and competency dimensions of issue ownership and individual vote choice for each major party within the 2015 election. This thesis finds mixed results when assessing which dimension serves as a better predictor compared to the other. The competency dimension performs as a better predictor for the issues of health care and crime. However, the associative dimension emerges as a better predictor for the environment and education issues. This illustrates how the 2015 case adds further variation to the comparative issue ownership literature when pondering which dimension emerges as a better predictor. These findings illustrate a Canadian case where associative issue ownership voting emerges, and that during the 2015 election the Liberal Party emerges as having issue ownership over the most issues compared to the Conservatives and NDP when considering both the associative and competency dimensions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".