Effective Policy Applications of Psychological Science: Drawing Parallels between COVID-19 and Climate Change
Bibliographic record
Abstract
In 2021, the world continues to face a serious, widespread challenge from the COVID-19 pandemic. Governments and civil society are grappling with unprecedented impacts on healthcare and the economy as well as restrictions of normal social interactions of millions. Still, the climate emergency has not rested. Unless addressed, carbon levels will continue to rise through this pandemic, the development and disbursements of vaccines, and the next pandemic. From a psychological perspective, there are many commonalities between the current COVID-19 pandemic and the ongoing crisis of climate change. This whitepaper begins by summarizing the broad similarities between these two crises. From there, we draw parallels between COVID-19 and climate change across four domains of psychological research. In doing this, we identify evidence-based approaches that policymakers and other key decision-makers can adopt to holistically respond to the two global crises of climate change and public health. We conclude with a broad discussion on the role of psychological science (and other social and behavioral sciences) in policy.
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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 | 0.058 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".