Summary of Waterloo Region Vote Intention, Spring 2021
Bibliographic record
Abstract
Headline Findings 1. Liberals lead in Waterloo Region in spring, federally and provincially. Federally, 28% of all respondents and 49% of all decided respondents said they would vote Liberal. The latter number is an increase of 11 percentage points over the Liberals’ 2019 results in Waterloo Region. 2. Provincially, the Liberals lead, but the the gap was much smaller. 22% of all respondents said they would support the Liberals compared to 18% for the PCs. This rose to 33% for decided voters for the Liberals and 29% for the PCs. This gap is just within the study’s margin of error 3. Share of 2019 Liberal voters who approved of Justin Trudeau’s handling of the pandemic was 13 percentage points higher than the share of 2018 PC voters who of Doug Ford’s handling of the pandemic (73% versus 60%) (see Tables 2 and 4). Trudeau had better evaluations from his most recent voters than Ford did. 4. Controlling for past federal and provincial votes, approval of handling the pandemic was a significant predictor of whether a voter would vote for both parties (see Tables 5, 6) 5. Conclusion: The Trudeau government’s handling of the pandemic helped its political standings and the Ford government’s handling of the pandemic hurt its chances in Waterloo Region. 6. Waterloo Region is an important bellwether set of ridings in Ontario. Table 8 and 9 show the number of ridings since 2003 in provincial and general elections that have returned members whose party formed the government.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".