How a Deadly Pandemic Cleared the Air: Narratives and Practices Linking COVID-19 with Air Pollution and Climate Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent COVID-19 pandemic revealed the intricate connections between human and planetary health. Air pollution cleared over the countries ordering lockdowns of nonessential businesses to flatten the curve of the pandemic. The links between pandemics and pollution are not obvious at first, yet the two phenomena have several characteristics in common. Both pandemics and pollution originate from specific locations but then spread globally, and both are human-induced rather than natural–hazard disasters. I examine narratives and practices linking COVID-19 with air pollution and climate change as the pandemic unfolds. I compare these findings with research on the Black Death plague in Europe and the air pollution in China’s Haze City. Applying the analytical frameworks from these two studies, I analyze media articles and reports on COVID-19 to explore risk experience, stress behaviours, and resistant discourse during the adaptive cycles of the pandemic to gain insights into current and future changes to sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it