Bibliographic record
Abstract
The chapters in this collection attempt to survey and analyze some of the key issues in central banking as of early 2009. Clearly, the ongoing global financial crisis has, as this is written, raised a whole set of new questions that are likely to be debated for years to come. Nevertheless, readers will notice that, in 2007, when a conference entitled “Frontiers in Central Banking” was held in Budapest at the National Bank of Hungary, the various papers, most of which appear in the present volume, already began to debate the larger questions of concern to central banks then and to monetary policy more generally today. Issues thought to be resolved, such as the role of central bank independence, have again resurfaced, as demonstrated by debate in the U.S. Congress over Federal Reserve Bank actions in 2008 and 2009, and how to hold that institution more accountable. As this book went to press, a bill was making its way through the U.S. Congress requiring the Fed to become more transparent. The state of the art as it pertains to central bank transparency is also addressed in the present volume. Other “big” questions, still unresolved, such as how to think about financial-system stability, its measurement, and its implications, are also front and center in this volume, as is the future of a monetary policy strategy focused on delivering low and stable inflation and the prospects of replacing it with price-level targeting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.118 | 0.083 |
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".