Bibliographic record
Abstract
There has been little discussion of central bank accountability in recent decades because monetary policy has been seen as an essentially technical problem. Yet, during the 2008 financial crisis and the economic dislocations that ensued, central banks gained considerably in authority—bailing out failing institutions, using unorthodox monetary tools, and wading into sovereign debt crises. At the same time, the financial crisis and the slow recovery that has followed have revealed just how uncertain and volatile the global economy can be—a situation that poses new dilemmas for monetary policy. This article looks at the existing model of central bank accountability and finds it wanting in this new, more uncertain environment. Because the principle of central bank independence involves a very narrow set of objectives—generally focused on an inflation target—and very few opportunities for sanction, the main mechanism for accountability is that provided by the publication of information about the bank's deliberations and activities. In an era of increased economic uncertainty, when central bankers themselves admit that simple rules and models are no longer adequate, a narrow, transparency-based form of accountability is not sufficient. I suggest that we need a thicker, more robust form of accountability that fosters more deliberation and debate, ensures that central banks are answerable to their publics, and broadens the standards by which they are judged.
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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 | 0.083 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".