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Record W4401473578 · doi:10.1515/9780691255446

Visions of Financial Order

2024· book· en· W4401473578 on OpenAlexaboutno aff
Kim Pernell

Bibliographic record

VenuePrinceton University Press eBooks · 2024
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsVisionOrder (exchange)BusinessEconomicsFinanceSociology

Abstract

fetched live from OpenAlex

How differences in national financial regulatory systems emerged from divergent beliefs about economic order and prosperity The global financial crisis of the late 2000s was marked by the failure of regulators to rein in risk-taking by banks. And yet regulatory issues varied from country to country, with some national financial regulatory systems proving more effective than others. In Visions of Financial Order , Kim Pernell traces the emergence of important national differences in financial regulation in the decades leading up to the crisis. To do so, she examines the cases of the United States, Canada, and Spain—three countries that subscribed to the same transnational regulatory framework (the Basel Capital Accord) but developed different regulatory policies in areas that would directly affect bank performance during the financial crisis. In a broad historical analysis that extends from the rise of the first modern chartered banks in the 1780s through the major financial crises of the twentieth century and the Basel Capital Accord of 1988, Pernell shows how the different (and sometimes competing) principles of order embedded in each country’s regulatory and political institutions gave rise to distinctive visions of order and prosperity, which shaped subsequent financial regulatory design. Pernell argues that the different worldviews of national banking regulators reflected cultural beliefs about the ideal way to organize economic life to promote order, stability, and prosperity . Visions of Financial Order offers an innovative perspective on the persistent differences between regulatory institutions and the ways they shaped the unfolding of the 2008 global financial crisis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0010.001
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 head0.028
GPT teacher head0.201
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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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