Prediction of Canadian Federal Election Results Based on Multilevel Regression and Post-Stratification
Bibliographic record
Abstract
In democratic countries like Canada, elections provide eligible citizens (aged 18 or older) the opportunity to vote and elect their leader. Since different political parties have distinct ideologies, election outcomes have significant societal impacts, making election result predictions crucial. This study aims to predict whether the Liberal Party will maintain its victory in the 2025 Canadian federal election using a multilevel regression model combined with post-stratification. The data for this research comes from the 2021 Canadian Election Study (CES) and the General Social Survey (GSS), with the cleaned datasets including variables such as age, gender, education, and province. Through the constructed multilevel logistic regression model and post-stratification adjustments, the results show that approximately 26.63% of Canadian citizens will vote for the Liberal Party in the next Canadian federal election. This prediction aligns with the hypothesis that the Liberal Party will not win the upcoming federal election. However, some variables in the model are not statistically significant, and the data is somewhat outdated. Future research should consider incorporating more variables and updated data.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".