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Record W2072479326 · doi:10.11130/jei.2015.30.1.1

Basel III in Reality

2015· article· en· W2072479326 on OpenAlexaboutno aff
Michele Fratianni, John C. Pattison

Bibliographic record

VenueJournal of Economic Integration · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Regulation and Crises
Canadian institutionsnot available
FundersEuropean CommissionStrong
KeywordsExpansiveClubTransparency (behavior)Basel IIIFinancial regulationHarmonizationEconomicsOperational riskAccountingLevel playing fieldSoft lawOligopolyCompetition (biology)BusinessInternational economicsPolitical scienceCapital requirementFinancial systemFinanceMicroeconomicsRisk managementLawInternational law

Abstract

fetched live from OpenAlex

Financial regulation has shifted from a system as an oligopoly dominated by the G2/G5 to expanded clubs like the Basel Committee for Banking Supervision. Expansive clubs have to agree to terms that are closer to the preferences of soft-regulation members. Yet, once a global agreement on minimum standards, such as Basel III, is reached, the task is left to national or regional regulators. Deviations from the Basel III standards are bound to occur; the complexity of the agreement will facilitate an asymmetric implementation of national regulation and supervision. Countries like Australia, Canada, the United Kingdom, the United States, and some Scandinavian countries have chosen higher standards. On the other hand, we should expect deviations to take place in member countries of the Eurozone that are heterogeneous having different preferences and trade-off between regulatory stringency and economic activity. The requirements of both global clubs and regional club regarding transparency, monitoring, and a level playing field will also cause a collision. This paper reports examples of heterogenous applications of supervisions and reforms.

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)0.018
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0130.006
Open science0.0020.004
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0800.057

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 head0.091
GPT teacher head0.283
Teacher spread0.192 · 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 designTheoretical or conceptual
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

Citations11
Published2015
Admission routes1
Has abstractyes

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