MétaCan
Menu
Back to cohort
Record W1556467697 · doi:10.18288/1994-5124-2016-4-01

Fragile by design: The Political Origins of Banking Crises and Scarce Credit

2016· article· en· W1556467697 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconomic Policy · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsFinancial systemBusinessEconomicsPolitical science

Abstract

fetched live from OpenAlex

Why are banking systems unstable in so many countries--but not in others? The United States has had twelve systemic banking crises since 1840, while Canada has had none. The banking systems of Mexico and Brazil have not only been crisis prone but have provided miniscule amounts of credit to business enterprises and households. Analyzing the political and banking history of the United Kingdom, the United States, Canada, Mexico, and Brazil through several centuries, Fragile by Design demonstrates that chronic banking crises and scarce credit are not accidents due to unforeseen circumstances. Rather, these fluctuations result from the complex bargains made between politicians, bankers, bank shareholders, depositors, debtors, and taxpayers. The well-being of banking systems depends on the abilities of political institutions to balance and limit how coalitions of these various groups influence government regulations. Fragile by Design is a revealing exploration of the ways that politics inevitably intrudes into bank regulation. Charles Calomiris and Stephen Haber combine political history and economics to examine how coalitions of politicians, bankers, and other interest groups form, why some endure while others are undermined, and how they generate policies that determine who gets to be a banker, who has access to credit, and who pays for bank bailouts and rescues.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.258
Teacher spread0.223 · 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