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Record W4410631839 · doi:10.1080/13563467.2025.2504399

Communication tools: a genealogy of quantitative easing

2025· article· en· W4410631839 on OpenAlexfundno aff
Will Bateman

Bibliographic record

VenueNew Political Economy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsQuantitative easingGenealogyEconomicsPolitical scienceKeynesian economicsHistoryMonetary policyCentral bank

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
Science and technology studies0.0030.027
Scholarly communication0.0070.011
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.089
GPT teacher head0.396
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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