Bibliographic record
Abstract
1 Thanks for the generous introduction, Howard. I’m delighted to have this opportunity to speak with all of you today, and especially to make my first official visit to Fargo as president of the Federal Reserve Bank of Minneapolis. Of course, as someone who grew up in Winnipeg, I am very familiar with Fargo and the Red River Valley. My family made many trips down here over the years. I found in Wikipedia that the West Acres Mall opened on August 2, 1972. I can’t remember for sure, but it’s certainly possible that we visited the mall on August 3! I’m especially pleased that one of the members of our Minneapolis Board of Directors, Howard Dahl, was here today to introduce me. As you just heard from Howard, he and other members of our board play an important role in the Federal Reserve System, as do members of our advisory councils on agriculture, and small business and labor. I won’t mention all the members of those groups, past and present, who are in attendance today, for the list is long, but I thank you for your service. As Howard just mentioned, I became president of the Federal Reserve Bank of Minneapolis last October. Here’s the start of a rather typical conversation that I would have had with my friends and relatives last fall. “Congratulations! That’s fantastic. Now, what is it that
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.466 | 0.257 |
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".