Summary Findings of the 2022 Ontario Provincial Election Survey
Bibliographic record
Abstract
The cost of housing in Ontario has steadily increased over the past decade, although the most dramatic increase occurred during the COVID-19 pandemic. For the average Ontarian who did not already own their own home, this dramatic surge in prices effectively locked them out of the housing market. This begs the questions: “what caused the surge in housing prices?” and “what can be done to fix it?”. This situation captured the attention of MPPs during the 2022 Ontario Provincial election, offering a multitude of various policy solutions for alleviating the housing crisis. Under this backdrop, LISPOP conducted a survey to capture the attitudes of Ontarians on increasing housing costs, as well as identifying the cause and possible solutions. These attitudes were analyzed by vote intention, housing status and issue salience. We found that there is more consensus than division on the significance of different causes (investors, NIMBY-ism and environmental protection) and solutions (increasing supply and tax policies) to address housing costs. However, the most fascinating results included some notable differences between respondents with varying party identification and homeowning status, especially attitudes towards immigration, investment in public housing and rent control.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".