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Record W3158155044 · doi:10.31235/osf.io/bms5n

Adding rooms onto a house we love: Central banking after the Global Financial Crisis

2018· article· en· W3158155044 on OpenAlexaff
Juliet Johnson, Vincent Arel‐Bundock, Vladislav Portniaguine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsCredibilityFinancial crisisMonetary policyInflation (cosmology)Financial systemFinancial stabilityResilience (materials science)Core (optical fiber)Price of stabilityCentral bankIndependence (probability theory)Psychological resilienceBusinessMonetary economicsEconomicsKeynesian economicsPolitical scienceEngineeringLawPsychology

Abstract

fetched live from OpenAlex

This article examines the extent to which central bankers have been willing and able to rethink their beliefs about monetary policy in the wake of the Global Financial Crisis. We show that despite the upheaval, the core pre-crisis monetary policy paradigm remains relatively intact: central bankers believe that they should primarily pursue price stability through targeting low inflation in a transparent manner, and that they need operational independence to achieve this goal. In a bid to address post-crisis conditions and maintain their credibility, however, central bankers have also layered new elements onto the old core. We document both the resilience of pre-crisis beliefs and the process of layering using computer-assisted text analysis and qualitative analysis of 13,586 speeches given between 1997 and 2017 by central bankers from around the world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.006

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 head0.020
GPT teacher head0.226
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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