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Record W2889441694 · doi:10.1016/j.dib.2018.08.061

European Central Bank׳s monetary policy decisions: A dataset of two decades of press conferences

2018· article· en· W2889441694 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsPolytechnique MontréalHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaHEC Montréal
KeywordsMonetary policyContext (archaeology)InstitutionStatement (logic)Political scienceCentral bankLibrary sciencePublic administrationEconomicsLawComputer scienceMacroeconomicsHistory

Abstract

fetched live from OpenAlex

The dataset gathers all press conferences in text form made by the first three Presidents of the European Central Bank since its inception in 1998. Press conferences are composed of two elements: (1) introductory statements and (2) questions and answers (Q&A) from journalists. They serve as the main communication vehicle about the monetary policy decision of the ECB׳s Governing Council. From 1998 to 2016, a total of 205 press conferences have been delivered. The dataset is structured into two main sections: (1) 205 introductory statement of the Presidents of the ECB explaining the monetary policy of the Institution and (2) 205 answers provided by the Presidents to the journalists, hence a total of 410 statements (914,499 words or 3050 pages) about the monetary policy and its context, in text form.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.083
GPT teacher head0.341
Teacher spread0.258 · 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