Prediction for Canadian federal election aided by Canadian Community Health Survey
Bibliographic record
Abstract
This project aims to develop predictive models for Canadian federal elections. We begin with explanatory analyses of two sets of data: some publicly accessible election data and some extracted data from the Canadian Community Health Survey (CCHS) 2007-2018 on life satisfaction and other potentially associated social-demographics. We propose to predict for federal election outcomes using the information on longitudinal Canadian life satisfaction. Specifically, we model the federal election outcome for a riding in change from its previous election jointly with its longitudinal life satisfaction since the previous election. Election data from years 2008 and 2011 and the CCHS data of 2008-2011 are employed to fit the model via both the two-stage estimation and the maximum likelihood estimation by the Monte Carlo EM algorithm. The analysis results indicate that life satisfaction is an important factor in election prediction. It appears that young adults are more likely to vote for a change but male voters are less likely to do so. Using voter information or CCHS respondent's information to model the election outcomes produce different estimation results. Two applications of the proposed approach are presented to further illustrate the proposed joint modeling approach.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".