Media-Based Post-Event Impact Analysis of the 2021 Heat Dome in Canada
Bibliographic record
Abstract
The unprecedented 2021 Heat Dome caused wide-ranging and long-lasting impacts in western Canada, including 619 confirmed heat-related deaths in British Columbia, a doubling of emergency medical calls, increased hospitalisations, infrastructure failures and stress on plants and animals. However, such varied socio-economic consequences of extreme heat can be challenging to capture using a single post-event analysis method. Therefore, there is a need to explore alternative approaches and data sources. Using the 2021 Heat Dome as a case study, a post-event analysis using online news media articles (n = 2909) from 5 subscription news databases and a grey literature search was conducted to identify the socio-economic impacts of the extreme heat event in Canada. The articles reported a wide range of effects to the natural environment (n = 1366), social infrastructure and services (n = 1121), human health (n = 1074), critical infrastructure (n = 988) and the private sector (n = 165). The media-based post-event analysis captured various impacts, some of which have not been identified through other data sources and approaches. Overall, we show that media analysis can complement traditional post-event analysis methods and provide additional perspectives to governments and public health and safety officials.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".