Fragile by design: The Political Origins of Banking Crises and Scarce Credit
Bibliographic record
Abstract
Preface ix SECTION ONE No Banks without States, and No States without Banks 1 If Stable and Effi cient Banks Are Such a Good Idea, Why Are They So Rare? 3 2 The Game of Bank Bargains 27 3 Tools of Conquest and Survival: Why States Need Banks 60 4 Privileges with Burdens: War, Empire, and the Monopoly Structure of English Banking 84 5 Banks and Democracy: Britain in the Nineteenth and Twentieth Centuries 105 SECTION TWO The Cost of Banker-Populist Alliances: The United States versus Canada 6 Crippled by Populism: U.S. Banking from Colonial Times to 1990 153 7 The New U.S. Bank Bargain: Megabanks, Urban Activists, and the Erosion of Mortgage Standards 203 8 Leverage, Regulatory Failure, and the Subprime Crisis 256 9 Durable Partners: Politics and Banking in Canada 283 SECTION THREE Authoritarianism, Democratic Transitions, and the Game of Bank Bargains 10 Mexico: Chaos Makes Cronyism Look Good 331 11 When Autocracy Fails: Banking and Politics in Mexico since 1982 366 12 Infl ation Machines: Banking and State Finance in Imperial Brazil 390 13 The Democratic Consequences of Infl ation-Tax Banking in Brazil 415 SECTION FOUR Going beyond Structural Narratives 14 Traveling to Other Places: Is Our Sample Representative? 451 15 Reality Is a Plague on Many Houses 479 References 507 Index 549
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".