Introduction: the new political economy of central banks: reluctant Atlases?
Bibliographic record
Abstract
After gaining independence from political authorities, the past decades, central banks in most of the Global North and some in the Global South have taken on additional goals, acquiring unprecedented powers, many of them in response to crises and a lack of forceful action by the political authorities. Central banks have also been confronted with new issues, such as the greening of the economy and digital finance. They have rediscovered ‘old’ roles – i.e. acting as lender of last resort, overseeing payment systems, supervising banks, issuing currencies (in a digital format) – and have taken on new roles. These roles include: ‘crisis managers’ of first resort, backstopping banks, non-banks, states and fellow central banks; ‘recession fighters’ of second resort as well as ‘quasi’ fiscal authorities; supporters of the green and digital transition; ‘sui generis diplomats’ fostering international cooperation, while behaving as hesitant ‘geoeconomic actors’ in an increasingly geopoliticised world. In the ‘new political economy of central banking’, these institutions can be seen as ‘reluctant Atlases’, at times, suffering from a lack of connection to central fiscal authorities (experiencing ‘loneliness’) and goal overstretching. Recent geopolitical turmoil presents new challenges to the liberal international order to which central banks are still seeking to respond.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".