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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".