The Monetary and Financial Powers of States: Theory, Dataset, and Observations on the Trajectory of American Dominance
Bibliographic record
Abstract
This project transforms international financial statistics into a form useful for global political analysis. The authors first theorise four distinct faces of a sovereign state’s monetary and financial power resources: its international Creditor, Network, Currency, and Governance Capabilities. Each of these capabilities implies resources that incumbent political leaders potentially may employ to persuade, induce, or coerce others in pursuit of their larger foreign policy goals. They thus provide the means of international financial statecraft. The paper next summarises a new dataset, the Global Monetary and Financial Profiles of States (GMFPS), which creates measures for each of these concepts. Covering 180 countries from 1995–2013, the GMFPS dataset reports each state’s annual shares of global totals for 25 indicators and 5 composites, each corresponding to a national financial characteristic that leaders may choose to manipulate politically – albeit not without paying some costs, economic and/or political. The paper concludes with an initial analysis of global trends, which tend to confirm the slow relative decline of the reigning financial hegemon, the United States. The data also provide suggestive evidence of a typical financial life-cycle for major states, although one that is voluntaristic, not inevitable.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".