The FOMC versus the Staff: Where Can Monetary Policymakers Add Value?” American Economic Review, 98:2 (May), 230-35. 1 CROSS-SECTIONAL STANDARD DEVIATIONS AND CORRELATIONS Growth Unemp. π (CPI) π (GDP) Real GDP Growth 0.37 Unemployment Rate –0.40 0.16 CPI
Bibliographic record
Abstract
A key issue in monetary policymaking is the appropriate division of labor between the professional staff of the central bank and the appointed policymakers. Lars E. O. Svensson (1999) argues that the appropriate role of a policymaking group, such as the Federal Open Market Committee (FOMC) in the United States, is to make judgments about social welfare, taking as given the likely outcomes of different policies as estimated by the staff. In this division, the staff is relied upon to assess current and prospective economic conditions and to forecast the effects of different policies. Policymakers ’ only role is to decide which of the various options should be chosen.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".