The fragility of depoliticization: revisiting the history of Central bank inflation-management
Bibliographic record
Abstract
In recent years, it has sometimes seemed like the fate of economic depoliticization is up for grabs: the 2008 global financial crisis raised serious questions about the economic viability of technocratic economic strategies while the rise of populist parties has challenged their political sustainability. Even central banks, once the priests of economic depoliticization, began to waver by the early 2020s, paying some attention to inequality and climate change. Yet, as inflation picked up after 2022, central banks returned to their old depoliticizing strategies, reimposing strict inflation targets. As we watch central bankers outdo each other as monetary hawks, we may be tempted to see the return to depoliticization as natural and inevitable. This paper paints a different picture, showing that policies aimed at depoliticizing monetary policy have encountered as many failures as successes since the early 1980s. Drawing on recently declassified archival material, it examines three attempts to put depoliticized monetary policy into practice in the UK, seeking to understand why depoliticization failed so many times and then succeeded. If attempts to depoliticize monetary policy have had a rockier history than is generally assumed, the era of successful monetary depoliticization was not only surprisingly brief but may well be over.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".