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Record W3123225207 · doi:10.24926/265535.1106

Regulating Financial Change: A Functional Approach

2016· article· en· W3123225207 on OpenAlexfundno aff
Steven L. Schwarcz

Bibliographic record

VenueMinnesota law review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
FundersUniversity of TorontoQueen Mary University of London
KeywordsFinancial regulationCorporate governancePoliticsFinancial marketGlobal financial systemFinancial servicesFinanceOrder (exchange)LawEconomicsPolitical scienceSociologyManagement

Abstract

fetched live from OpenAlex

How should we think about regulating our dynamically changing financial system? Existing regulatory approaches have two temporal flaws. The obvious flaw, driven by politics and human nature (and addressed in other writings), is that financial regulation is overly reactive to past crises. This article addresses a less obvious but arguably more fundamental flaw: that financial regulation is normally tethered to the financial architecture, including the distinctive design and structure of financial firms and markets, in place when the regulation is promulgated. In order to effectively address future crises, this article argues, financial regulation must transcend that time-bound architecture. This could be done by regulating the underlying economic functions of the financial system—the provision, allocation, and deployment of financial capital—as well as the financial system’s capacity to serve as a network within which those functions can be conducted. The article analyzes how to design and implement such a “functional” approach to financial regulation. Although this approach is primarily normative, it provides regulatory ordering principles that should have practical utility—not only as a set of standards to inform actual regulatory design but also as a counterweight to the prevailing view that macroprudential regulation of systemic risk can be adequately served by an ad hoc assortment of regulatory “tools.”

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.068
GPT teacher head0.242
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2016
Admission routes1
Has abstractyes

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