Climate Change in the 2019 Canadian Federal Election
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it