Looking Forward: The Path for Monetary Policy
Bibliographic record
Abstract
The U.S. economy is on solid footing. The labor market is nearing full employment, and inflation should move back toward the Federal Open Market Committee’s target. A likely gradual removal of highly accommodative monetary policy could begin at any upcoming FOMC meeting. However, the exact timing will be driven by the incoming data. The following is adapted from a presentation by the president and CEO of the Federal Reserve Bank of San Francisco to the New York Association for Business Economics in New York on May 12. It’s a pleasure to be in New York. I’d like to give an overview of the economy today and where I see us going. I’ll address some of the questions I’ve been hearing most frequently, and talk about the trajectory of monetary policy going forward. Disappointing first quarter There are two questions I’m asked on an almost daily basis right now, so I’ll preempt the Q&A and get to them right off the bat. One of them is: Given first-quarter weakness, am I revising my outlook for the year? So far, I’ve been relatively upbeat about the economic outlook and the direction we’re heading. The answer leads me to something I say frequently: We need to look at data over the longer term. We can’t get distracted by blips and temporary downs—or ups for that matter.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".