Linking crises: Connections between climate change and COVID-19 during American, Canadian, Dutch, and Lithuanian national elections (2020-2021)
Bibliographic record
Abstract
Crisis responses are created in reference to the meanings of other crises. We develop the notion of ‘linking crises’ to capture this phenomenon and apply it to two contemporary global crises: climate change and COVID-19. Concretely, we study four crisis linkage dimensions in American, Canadian, Dutch, and Lithuanian party manifestos for national elections from the pandemic heydays (2020–2021): (1) how often links were drawn; (2) the issues they were related to; (3) the argumentative strategies for making connections; and (4) and the political level on which this occurred. We find many cross-national similarities. For instance, in every country links with economic and environmental issues were very present, an argumentative trope of ‘building back better’ was employed by various political parties, and the majority of the connections were made at supra-national levels. These findings bring up the question of when cross-national parity in crisis responses does (not) occur.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".