Do Economists Reach a Conclusion on Free-Banking Episodes?
Bibliographic record
Abstract
How should banks be regulated? Must governments tightly regulate banks to prevent financial panics, or is little or no regulation best? Can private banks be trusted to issue paper money or must this activity be a government monopoly? Theory can help answer these questions, but increasingly in recent years economists have turned to the natural experiments of history to find out how well free banking systems, or more accurately lightly regulated banking systems, have worked in practice. We now have numerous studies of lightly regulated banking in Scotland, the United States, Canada, and many other countries. As usual, research has produced new questions and heated controversies. The resulting ruckus tends to obscure the areas in which research has produced a consensus. Here we try to separate the areas where there is a consensus from areas where research is still in its early stages.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".