Global polycrisis: the causal mechanisms of crisis entanglement
Bibliographic record
Abstract
Abstract Multiple global crises – including the pandemic, climate change, and Russia's war on Ukraine – have recently linked together in ways that are significant in scope, devastating in effect, but poorly understood. A growing number of scholars and policymakers characterize the situation as a ‘polycrisis’. Yet this neologism remains poorly defined. We provide the concept with a substantive definition, highlight its value-added in comparison to related concepts, and develop a theoretical framework to explain the causal mechanisms currently entangling many of the world's crises. In this framework, a global crisis arises when one or more fast-moving trigger events combine with slow-moving stresses to push a global system out of its established equilibrium and into a volatile and harmful state of disequilibrium. We then identify three causal pathways – common stresses, domino effects, and inter-systemic feedbacks – that can connect multiple global systems to produce synchronized crises. Drawing on current examples, we show that the polycrisis concept is a valuable tool for understanding ongoing crises, generating actionable insights, and opening avenues for future research. Non-technical summary The term ‘polycrisis’ appears with growing frequently to capture the interconnections between global crises, but the word lacks substantive content. In this article, we convert it from an empty buzzword into a conceptual framework and research program that enables us to better understand the causal linkages between contemporary crises. We draw upon the intersection of climate change, the covid-19 pandemic, and Russia's war in Ukraine to illustrate these causal interconnections and explore key features of the world's present polycrisis. Technical summary Multiple global crises – including the pandemic, climate change, and Russia's war on Ukraine – have recently linked together in ways that are significant in scope, devastating in effect, but poorly understood. A growing number of scholars and policymakers characterize the situation as a ‘polycrisis’. Yet this neologism remains poorly defined. We provide the concept with a substantive definition, highlight its value-added in comparison to related concepts, and develop a theoretical framework to explain the causal mechanisms currently entangling many of the world's crises. In this framework, a global crisis arises when one or more fast-moving trigger events combines with slow-moving stresses to push a global system out of its established equilibrium and into a volatile and harmful state of disequilibrium. We then identify three causal pathways – common stresses, domino effects, and inter-systemic feedbacks – that can connect multiple global systems to produce synchronized crises. Drawing on current examples, we show that the polycrisis concept is a valuable tool for understanding ongoing crises, generating actionable insights, and opening avenues for future research. Social media summary No longer a mere buzzword, the ‘polycrisis’ concept highlights causal interactions among crises to help navigate a tumultuous future.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".