Bibliographic record
Abstract
There has been a significant regionalization of international trade. In 1990, 37 percent of the foreign trade of Canada, Mexico, and the United States was bilateral trade between pairs of those three countries; by 2004, the figure had risen to nearly 44 percent. In 1990, 29 percent of the foreign trade of thirteen East Asian countries was bilateral trade between pairs of those same countries; by 2004, the figure had risen to 39 percent. (See Table 1.1.) Some but not all of this increase in regional trade reflects the formation of preferential trading arrangements, such as the North American Free Trade Area (NAFTA) and the Association of South East Asian Nations (ASEAN). This book asks whether we should expect to see an analogous regionalization of the international monetary system over the next one or two decades, the form or forms that it might take, and the potential benefits and costs viewed from the standpoint of the participants. It also asks how regional monetary integration might affect outsiders, including, most important, the United States, because of the key role played by the U.S. dollar in the global monetary system. Why do we ask these questions now? Over the past several years, a number of countries have given up their national currencies and replaced them either with a multinational monetary union or with a prominent international currency such as the U.S. dollar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.378 | 0.240 |
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".