Bibliographic record
Abstract
It has become commonplace to suggest that we are living through a kind of ‘global polycrisis’. This article shows how the mobilisation of international finance has been at the core of political responses to four crises that are often cited as key constituents of this phenomenon: the global pandemic, the Russian invasion of Ukraine, the planetary climate crisis, and the growing Sino-American geoeconomic fracture and rivalry. It also demonstrates how international finance has been mobilised for very different purposes in each case. Further, the article reveals how these distinctive forms of mobilisation have generated important innovations in the financial practices of public authorities that have both strengthened global financial cooperation and led to a more fragmented international financial order. With Trump's re-election, the fragmentary trends now have the upper hand and may soon be reinforced by an impending fifth crisis: a global financial meltdown. Taken together, these arguments highlight: the centrality of international finance in the contemporary global polycrisis; the diverse roles it plays; the significance of crises as catalysts for innovations in financial practices; the complexity of the dynamics of polycrises; and the challenges associated predicting their future trajectory.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".