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The Global Financial Crisis and Central Bank Speak

2014· book-chapter· en· W2487911796 on OpenAlexaff
Pierre L. Siklos

Bibliographic record

VenueAdvances in linguistics and communication studies · 2014
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsFinancial crisisMonetary policyCentral bankFinancial systemFinancial stabilityState (computer science)EconomicsForward guidanceQuantitative easingPolitical scienceEconomic policyBusinessInflation targetingMonetary economicsMacroeconomicsCredit channel

Abstract

fetched live from OpenAlex

Words are critical in how the public perceives the work of central banks and the quality of monetary policy. Press releases that accompany policy rate decisions and, where available, the minutes of central bank committee meetings, are focal points for the media in public discussions about the conduct of monetary policy. Using data from five countries, this chapter examines whether the language used by central banks has changed since the Global Financial Crisis (GFC) began. Briefly, the findings show that concerns about financial stability peaked just as the global financial crisis reached its zenith. However, concerns over uncertainty about the current and anticipated state of the economy have also risen over time. More generally, central bank speak became more aggressive throughout the crisis years. More conventional expressions about the current stance of monetary policy took a back seat to other concerns in central bank policy statements and minutes.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.005
Scholarly communication0.0070.006
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2014
Admission routes1
Has abstractyes

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