Bibliographic record
Abstract
Abstract. Accumulating debt is usually harmful for states, but a cyclical deficit policy and large-scale borrowing have been beneficial for the United States. While structural changes in the international political economy may cap America's future ability to process debt, an empirical analysis of the economic dimensions of hegemony over the last quarter century shows unambiguously that the hegemon reaps disproportionate gains in the area of trade and investment. This finding provides new insight on whether it is advantageous to be a hegemon. Résumé. Les États pâtissent généralement de l'accumulation des dettes, mais une politique de déficit cyclique et le recours à de larges emprunts ont pourtant été bénéfiques aux États-Unis. La capacité future de la puissance américaine à gérer sa dette sera peut-être entamée par les changements structurels subis par l'économie politique mondiale. Toutefois, l'analyse empirique des dimensions économiques de la situation d'hégémonie durant les vingt-cinq dernières années met à jour, et sans ambiguïté aucune, les gains disproportionnés générés par l'hégémon dans les domaines du commerce et de l'investissement. Cette recherche apporte un éclairage nouveau au débat sur les avantages liés à la position d'hégémon.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".