Bibliographic record
Abstract
The 30 th anniversary of the Cato Institute’s monetary conference series provides an excellent opportunity to take stock of what we have learned about monetary policy in the past 30 years and to draw lessons for the next 30 years. Considering the overall performance of the American economy, the past 30 years divide naturally into two parts. During the first part—roughly the first two-thirds—economic performance was quite good, but during the second part it was quite poor. In terms of monetary policy, there is a corresponding natural division with a steadier rules-based approach to policy in the first part and a much less predictable discretionary approach to policy in the second. The policy implication of this experience thus jumps out at you. To be sure, however, one needs to work carefully through the facts and follow the relationship between economic performance and monetary policy. Economic Performance Let’s start with some charts which illustrate the key facts. Figure 1 shows the growth rate of real GDP from quarter to quarter in the United States. It is like an EKG for the American economy. It shows that the volatility of GDP growth declined markedly in the 1980s and 1990s. 1 This is a written version of a luncheon address given at the Cato Institute’s 30th Annual Monetary
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.145 | 0.079 |
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".