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Record W2915187252 · doi:10.2866/112727

The use of the Eurosystem's monetary policy instruments and operational framework since 2012

2017· article· en· W2915187252 on OpenAlexaboutno aff
Inmaculada Molina Álvarez, Fabio Casavecchia, Marino De Luca, Alexander Duering, Fabian Eser, Caspar Helmus, Christophe Hemous, Niko Herrala, Julija Jakovicka, Michelina Lo Russo, Filippo Pasqualone, Marc Rubens, Rita Isabel Prior Soares, Fabrizio Zennaro, Beatrice Amaladasse, Geneviève Deanaz, Benoît Hallinger, William Hilebrand, Annette Kamps, Danielle Kedan, Judith Kilp, Evi Koch, Louisa König, Rolf Pauli, Dion Reijnders, Marc Resinek, Martin Treinies, Olivier Vergote

Bibliographic record

VenueEconstor (Econstor) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEuropean Monetary and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCollateralMonetary policyMarket liquidityBalance sheetAsset (computer security)Context (archaeology)BusinessCounterpartyQuarter (Canadian coin)EconomicsMonetary economicsFinancial systemFinanceCredit riskComputer science

Abstract

fetched live from OpenAlex

This paper provides a comprehensive overview of the use of the Eurosystem's monetary policy instruments and the operational framework from the third quarter of 2012 until the first quarter of 2016. The paper reviews the context of Eurosystem market operations, counterparty and collateral framework, participation in tender operations, recourse to standing facilities, patterns of reserve fulfilment, outright asset purchase programmes, as well as the impact of the ECB's monetary policy implementation on the Eurosystem's balance sheet and liquidity conditions.

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)0.015
metaresearch head score (Gemma)0.023
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0010.002
Scholarly communication0.0110.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.227
Teacher spread0.189 · 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
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

Citations9
Published2017
Admission routes1
Has abstractyes

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