Climate Change in the 2019 Canadian Federal Election
Bibliographic record
Abstract
In the weeks before the 2019 federal election, climate change strikes occurred in Canada and across the globe, which may have increased the salience of this policy issue. We use two data sources to examine the role of climate change in the 2019 federal election: a representative survey of 1500 Canadians and 2109 Facebook posts from the five major party leaders. After accounting for political ideology and region, we find that concern about climate change was a strong positive predictor of liberal support. We triangulate these findings by analyzing Facebook posts. We find that left-wing politicians were more likely to post about climate change and that posts about climate change received more likes, comments, and shares than other posts. This higher level of user engagement did not differ depending on which political party posted the climate change message. The combination of sources offers news insights into citizen-elite interactions and electoral outcomes. Climate change was important in the election, whether this importance was measured through survey data or user engagement with leaders’ climate change posts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".