The Climate Virus: Garnering Lessons from COVID-19 Towards Urgent Climate Action in Canada
Bibliographic record
Abstract
This two-step mixed methods research project investigated the Canadian response to two compounding global crises: the COVID-19 pandemic and the climate crisis. This research project asked: How can Canada’s COVID-19 experience inform a more urgent and effective approach to climate action? This work analyzed reflections from diverse members of Canadian civil society organizations (CSO) active on climate issues to capture cross-crisis lessons learned. Insights were gathered from 11 guided semi-structured interviews. Participants were also led through an imaginative futures exercise to envision a pandemic-level climate action plan in Canada. A thematic analysis of the results reveals key lessons about social supports, communication, and political polarization in times of crisis, and the key role the government plays as a supporter, communicator, and convenor in catalyzing change. Findings suggests there is an urgent need for a radical paradigm shift to materialize a more effective climate action plan in Canada.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.013 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".