Bibliographic record
Abstract
In the last few years, the Federal Reserve, the European Central Bank and the Bank of Canada have all initiated policy reviews to reassess monetary policy tools and ideas that were no longer working in a changing global economy. In this paper, I argue that the debates that shaped these reviews can best be understood as ‘knowledge controversies’ in which some of the foundational metrics that monetary policies rely on themselves became the subject of debate. This unsettling of existing economic metrics poses not just technical but also political problems for central banks. These debates have eroded their efforts to depoliticise key issues, revealing the fragility of their technical fixes as metrics themselves can become the source of political disagreement both within the central banks themselves and in the wider public. As we enter an era of even more intense inflation-driven debates about central banking expertise and authority, these reviews provide insights into the epistemic dilemmas that central banks confront, suggesting that they will be dealing with knowledge controversies for some time to come.
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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 | 0.080 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".