Not a steamroller, a 3D process: Scientization at the Bank of England
Bibliographic record
Abstract
Abstract This article investigates the scientization process in central banks, using the Bank of England (BoE) as a case study. Its main goal is to clarify the interactions and tensions among three dimensions of scientization: contributory, policymaking and legitimizing. To do so, we outline an ideal type of contributory scientization in central banks, whereby they become active contributors to science. The article derives empirically observable characteristics for this ideal type, regarding leadership and staff profiles, use of internal resources, composition of external networks, and publication and discursive outputs. The BoE is then contrasted to this ideal type of a central bank thoroughly involved in contributory scientization . The empirical material includes archives and interviews as well as three databases providing quantitative information from the 1970s to 2019. We find that the development of contributory scientization is strategically motivated, often generating tensions with policymaking and legitimizing dimensions. Our findings suggest that scientization in central banks is best understood as a three-dimensional, non-linear process, rather than a steamroller.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".