Bibliographic record
Abstract
1 Thank you for the generous introduction. I’m delighted to have this opportunity to speak with you today, especially on this special occasion—the 25th anniversary of the UMACHA conference. So let me begin by congratulating all of you on the longevity of this important event, and also on its success. It goes without saying that there have been many changes in the payments industry over the last quarter century, and likewise many challenges. I am certain that having a venue like this to come together and learn has greatly benefited the industry, and I am proud of the role that we have played at the Federal Reserve Bank of Minneapolis to make this event happen. Most people probably don’t realize the extent to which the Federal Reserve is active in the payments system. For example, as you just heard, 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 you will do exactly? ” They were delighted when I began telling them about FedACH. But I don’t think I’ve been invited here today because of my expertise in matters relating
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.107 | 0.030 |
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".