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Record W4384131467 · doi:10.3390/jrfm16070329

“Decoding” Policy Perspectives: Structural Topic Modeling of European Central Bankers’ Speeches

2023· article· en· W4384131467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicComputational and Text Analysis Methods
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsTransparency (behavior)Central bankIndependence (probability theory)RhetoricMonetary policyFinancial crisisPolitical scienceInflation (cosmology)AccountingFinancial systemEconomicsMonetary economicsMacroeconomicsLaw

Abstract

fetched live from OpenAlex

This research analyzes the speeches and interviews of high-level European Central Bank decision makers from 1997 to 2021 to identify clusters of prominent topics. Transparency is a crucial aspect of modern monetary policy, and public speeches and interviews by central bankers play a vital role in achieving it. Our study employs structural topic modeling to compare the prevalence of topics during the Global Financial Crisis, pandemic crisis, and periods of heightened inflation. Additionally, we explore the impact of central bank independence on official rhetoric.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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