The Spectrum of (Poly)Crisis: Exploring polycrises of the past to better understand our current and future risks
Bibliographic record
Abstract
As the concept of polycrisis gains popularity among academics, policy-makers, and the general public, many questions linger about the utility, scope, and applicability of the term in different contexts. Building on prior work, we argue that crises can fruitfully be understood as existing along a spectrum, characterized by multiple different factors, with our modern polycrisis at one extreme. We illustrate this by surveying three historical periods with varying geographical scope – late 1st-millennium CE Mesoamerica, Late Medieval Eurasia, and the early modern Northern Hemisphere – arguing that these exhibited many, but not all, of the key characteristics that make up a polycrisis. We detail the experience of individual societies during these periods, focused on regions to highlight how stresses and dysfunction across multiple systems combined to produce devastating impacts and contrast these with the relatively mild experiences of others facing the same conditions. We highlight how the interaction of stresses across ecological, economic, social, and political systems produced disasters that further deepened these crises and so led to yet more disasters and further devastation. We illustrate how viewing these historical periods through a polycrisis lens can not only inform our understanding of the past but can produce valuable lessons for our modern world. The multi-faceted, far-reaching, and devastating consequences of our current polycrisis should not be viewed as entirely a recent phenomenon. Ultimately, we argue that studying historical polycrises as we do here can help us learn lessons from the past that allow us to hone strategies for addressing the comparable issues we face today and will continue to contend with for the foreseeable 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".