Understanding polycrisis: definitions, applications, and responses
Bibliographic record
Abstract
Abstract Non-Technical Summary The term ‘polycrisis’ is gaining attention among academics, policymakers, and the public. Unlike a single crisis, a polycrisis involves complex, interconnected risks across multiple regions and systems, often including ecological factors. This interconnectedness heightens the chances of widespread adverse outcomes or disasters, affecting various systems and triggering cascading effects. The article examines how traditional disaster studies concepts must be adapted for the polycrisis context and places historical events on a spectrum of such critical moments. It concludes with recommendations for communities to build resilience and respond democratically to these challenges. Technical Summary The term ‘polycrisis’ has entered the lexicon of a growing circle of academics, policymakers, and the public. Polycrisis is a state that encompasses a complex set of risks characterized by multiple, macroregional, and often ecologically embedded linkages between inexorably interconnected systems. The article reevaluates disaster studies concepts within this polycrisis framework, locates historical events along a spectrum of such moments, and offers recommendations for democratic resilience. Social Media Summary Discover how the term ‘polycrisis’ redefines our understanding of interconnected risks and informs new disaster response methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".