Federal Reserve Bank of Boston's 52nd Annual Economic Conference
Bibliographic record
Abstract
Good evening. I am pleased to be able to participate in the Federal Reserve Bank of Boston's 52nd annual economic conference, on the topic of inflation and the Phillips curve. Forecasting and controlling inflation are, of course, central to the process of making monetary policy. In this respect, policy makers are fortunate to be able to build on an intellectual foundation provided by extensive research and practical experience. Nonetheless, much remains to be learned about both inflation forecasting and inflation control. In the spirit of this conference, my remarks this evening will highlight some key areas where additional research could help to provide a still-firmer foundation for monetary policymaking. Before turning to those issues, however, I would like to provide a brief update on the outlook for the economy and policy, beginning with the prospects for growth. Despite the unwelcome rise in the unemployment rate that was reported last week, the recent incoming data, taken as a whole, have affected the outlook for economic activity and employment only modestly. Indeed, although activity during the current quarter is likely to be weak, the risk that the economy has entered a substantial downturn appears to
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.234 | 0.159 |
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".