Bibliographic record
Abstract
Over the last 20 years or so, the volatility of aggregate economic activity has fallen dramatically in most of the industrialized world. The timing and nature of the decline vary across countries, but the phenomenon has been so widespread and persistent that it has earned the label: ?the Great Moderation.? A growing body of research has focused on the Great Moderation and its possible explanations, especially as it applies to the U.S. experience. The literature documents the international dimension of this volatility reduction, but so far little is known about the possible causes from a cross-country perspective. Summers shows why the Great Moderation has indeed been a common feature of much of the industrialized world. Specifically, he focuses on the reduction in the volatility of GDP growth that occurred in the G-7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and Australia. He uses international evidence to evaluate the merits of three likely explanations. He concludes that, from an international perspective, good luck in the form of smaller energy price shocks is not a compelling explanation for widespread moderation of GDP growth volatility. Rather, the Great Moderation is more likely due to better monetary policy outcomes and improved inventory management techniques.
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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".