Bibliographic record
Abstract
For most of the twenty-first century, the world’s largest central banks have been acquiring vast quantities of government debt. From the 1930s–1970s, identical operations formed the financial backbone of deficit-funded public sector expansion. In the latter twentieth-century, monetised deficit-support became anathema to models of central bank independence built on the dominance of monetary authorities over fiscal agencies and an inflation-targeting regime operationalised through interest-rate setting. Massive public debt acquisitions re-commenced in Japan in 2001 and then spread throughout the advanced economies. Those 'quantiative easing' (QE) policies were launched in the shadow of system-wide bank failures and fiscal stimulus programmes funded by historic public deficits. Central banks justified QE using a theoretic nomenclature which emphasised the private-market orientation of their policies and omitted any fiscal-supporting function. This article documents the development of the communication strategies used by two first-mover central banks, the Bank of Japan and the US Federal Reserve, to re-package and explain debt monetisation techniques as market-neutral inflation-targeting exercises. It compares the internal and external communications of those central banks during the launch of QE programmes to produce a genealogy which explains how and why the fiscal-supporting functions of public debt purchases were obscured in favour of private-market effects.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".