Forecasting Change: Climate Connections in Canadian Extreme Weather Reporting
Bibliographic record
Abstract
Canadians from coast to coast are experiencing the consequences of the increase in extreme weather events, like the unprecedented 2023 forest fire season. With the impact these extreme events have on public life, the Canadian media cover them extensively, and thus they make up a significant proportion of the news as they occur. The number of these events is said to continue increasing, as the scientific consensus states climate change and rising temperatures will continue to influence the frequency and intensity of extreme weather. In recent years, scientists have begun conducting extreme event attribution (EEA) studies which link certain events to climate change. While some argue that journalists should mention these EEA studies in their coverage, others argue making explicit connections to climate change is enough to fully inform the public. Even with this newly emerging debate, little is known about how the Canadian media mention or connect climate change to extreme weather events. Using a text as data approach, with automated content analysis, this thesis explores how five Canadian media outlets cover four types of extreme weather events, by comparing reporting from 2019 and 2023. This work aims to examine how the media mention climate change, whether they connect extreme weather events to climate, and how they make that connection. The results show that overall, the proportion of climate connection used in the coverage of extreme events by the selected Canadian media increased from 2019 to 2023. Additionally, both the proportion of climate mentions and connections are greater than those found in existing studies.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".