Social–ecological systems approaches are essential for understanding and responding to the complex impacts of COVID-19 on people and the environment
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic is dramatically impacting planetary and human societal systems that are inseparably linked. Zoonotic diseases like COVID-19 expose how human well-being is inextricably interconnected with the environment and to other converging (human driven) social–ecological crises, such as the dramatic losses of biodiversity, land use change, and climate change. We argue that COVID-19 is itself a social–ecological crisis, but responses so far have not been inclusive of ecological resiliency, in part because the “Anthropause” metaphor has created an unrealistic sense of comfort that excuses inaction. Anthropause narratives belie the fact that resource extraction has continued during the pandemic and that business-as-usual continues to cause widespread ecosystem degradation that requires immediate policy attention. In some cases, COVID-19 policy measures further contributed to the problem such as reducing environmental taxes or regulatory enforcement. While some social–ecological systems (SES) are experiencing reduced impacts, others are experiencing what we term an “Anthrocrush,” with more visitors and intensified use. The varied causes and impacts of the pandemic can be better understood with a social–ecological lens. Social–ecological insights are necessary to plan and build the resilience needed to tackle the pandemic and future social–ecological crises. If we as a society are serious about building back better from the pandemic, we must embrace a set of research and policy responses informed by SES thinking.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".