How much did the Federal Reserve learn from history in handling the crisis of 2007-2008
Bibliographic record
Abstract
1 XXVIII Jornadas Anuales de Economia, November 7-8, 2013, Banco Central del Uruguay. 2 Rutgers University, NBER and The Hoover Institution, Stanford University. Michael Bordo is Ph D in Economics by the University of Chicago (1972). At the moment, he is Economics Professor and Director of the Center for Monetay and Financial History at Rutgers Univeristy, New Brunswick, New Jersey. He has occupied academic positions at South Carolina University and Carleton University in Ottawa, Canada. He has been a visiting professor at University of California in Los Angeles (UCLA), Carnegie Mellon, Princeton, Harvard, Cambridge (where he was professor of American History and Institutions) and visiting scholar at the IMF, the Reserva Federal of St. Louis and Cleveland, the Bank of Canada, the Bank of England and the BIS. Also he is Associated Researcher at the National Bureau of Economic Research (NBER). He has published a lot of articles in journals and ten books in monetary economics and economic history. He is editor of a series of books from Cambridge University Press: Studies in Macroeconomic History. REVISTA DE ECONOMIA, Vol. 21, No 1, Mayo 2014. ISSN: 0797-5546
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".