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Record W3150813413 · doi:10.1515/9781400835256

Unsettled Account: The Evolution of Banking in the Industrialized World since 1800

2010· article· en· W3150813413 on OpenAlexaboutno aff
Richard S. Grossman

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsBoomGrossmanBustNoticeFinancial systemFinancial crisisEconomicsRecessionBusinessWork (physics)Market economyEconomyPolitical scienceKeynesian economicsLawEngineering

Abstract

fetched live from OpenAlex

Commercial banks are among the oldest and most familiar financial institutions. When they work well, we hardly notice; when they do not, we rail against them. What are the historical forces that have shaped the modern banking system? In Unsettled Account, Richard Grossman takes the first truly comparative look at the development of commercial banking systems over the past two centuries in Western Europe, the United States, Canada, Japan, and Australia. Grossman focuses on four major elements that have contributed to banking evolution: crises, bailouts, mergers, and regulations. He explores where banking crises come from and why certain banking systems are more resistant to crises than others, how governments and financial systems respond to crises, why merger movements suddenly take off, and what motivates governments to regulate banks. Grossman reveals that many of the same components underlying the history of banking evolution are at work today. The recent subprime mortgage crisis had its origins, like many earlier banking crises, in a boom-bust economic cycle. Grossman finds that important historical elements are also at play in modern bailouts, merger movements, and regulatory reforms. Unsettled Account is a fascinating and informative must-read for anyone who wants to understand how the modern commercial banking system came to be, where it is headed, and how its development will affect global economic growth.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.011
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.217
Teacher spread0.193 · 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.

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

Citations17
Published2010
Admission routes1
Has abstractyes

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