Bibliographic record
Abstract
As the U.S. struggles with its first economic slowdown in a decade, so do most of the major industrialized countries. Japan is sliding again into recession, with third quarter GDP growth of -2.2%. Europe also seems to be slowing, with a third quarter growth rate of 0.4% for the euro area as a whole, and -0.6% for Germany in particular. ; This concurrent slowdown may not seem so surprising, given the increasing globalization and integration of the world's economies. For example, it may be that countries now face common shocks, such as a change in oil prices or a productivity slowdown; or, it is possible that the U.S. recession is spilling over to our major trading partners, as our demand for imports flags. ; In light of these possibilities, some have argued that macroeconomic policymakers need to take such common shocks and spillovers into consideration--and some even argue that it is appropriate to coordinate economic policies among countries. This topic of policy coordination, which received fairly little attention from researchers in the last ten years or so, has re-emerged as an important area of study. This Economic Letter examines the literature in this field, especially the recent and influential work of Obstfeld and Rogoff (2001). Their paper offers a very different perspective from the older literature on the topic, showing that increasing economic integration may not, ironically, mandate greater policy coordination.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.038 | 0.024 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".