Narayana Kocherlakota: Looking back at four years of Federal Reserve actions Speech by Mr Narayana Kocherlakota, President of the Federal Reserve Bank of
Bibliographic record
Abstract
Thank you for that generous introduction. It’s a huge pleasure to be back in Winnipeg. I’ve been gone a long time. How long? After living here for 13 years, I moved back to the States right after the Winnipeg Jets won their last AVCO Cup as champions of the World Hockey Association – that is, 1979. Consequently, my words today will be those of an American policymaker speaking on American policy issues. More specifically, I’ll provide a look back at Federal Reserve decision-making over the past few years. However, I’ll close with my views on how I think that the Federal Open Market Committee – the FOMC – can work to reduce the level of uncertainty surrounding future monetary policymaking. As always, any views I express here today are my own, and not necessarily those of others in the Federal Reserve System, including my colleagues on the Federal Open Market Committee. Some FOMC basics Let me begin with some basics about the Federal Reserve System. The Federal Reserve Bank of Minneapolis is one of 12 regional Reserve banks that, along with the Board of Governors in Washington, D.C., make up the Federal Reserve System. Our bank represents the ninth of the 12 Federal Reserve districts, and by area, we’re the second largest. Our
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".