Introduction [Unsettled Account: The Evolution of Banking in the Industrialized World since 1800]
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".