MétaCan
Menu
Back to cohort
Record W4319790816 · doi:10.1080/01402382.2022.2155906

Weaponisation of finance: the role of European central banks and financial sanctions against Russia

2023· article· en· W4319790816 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

VenueWest European Politics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaUniversiteit LeidenUniversité du Luxembourg
KeywordsSanctionsGeopoliticsFinancial systemCentral bankEuropean unionFinancial crisisPolitical scienceBusinessMonetary policyEconomicsInternational economicsEconomic policyMonetary economicsMacroeconomicsLawPolitics

Abstract

fetched live from OpenAlex

In response to Russia’s full-scale invasion of Ukraine, the Group of Seven (G7) countries and the European Union (EU) adopted a variety of financial sanctions, including the freezing of foreign reserve assets of the Central Bank of Russia held by other central banks. Drawing on a Principal-Agent framework and on speeches, newspaper articles and interviews with policy-makers, this study examines what it means for the ECB and the central banks of the Eurosystem to be involved in these sanctions. As a consequence of these actions, these central banks have been enlisted in monetary and financial warfare. Moreover, the three-fold objective of the ECB has de facto effectively been reweighted somewhat, as the focus on ‘price stability’ (primary objective) has become seemingly temporarily less prominent. Instead, the secondary and tertiary objectives have moved centre-stage, favouring geopolitical considerations.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.201
Teacher spread0.182 · 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