Bibliographic record
Abstract
INTRODUCTION Central banking is a twentieth-century phenomenon. According to Capie (1997), there were only 18 central banks at the beginning of the twentieth century. By 1950, there were 59 central banks, and by 1990 there were 161. At the beginning of the twentieth century, the U.S. Federal Reserve System had not yet been established; this would occur in 1914. The Bank of Canada came into being after the Great Depression, in 1934. Prior to the twentieth century, central banks were established as institutions with monopoly rights over money issuance. But if a critical element of central banking is the function of lender of last resort, then these institutions generally did not become central banks until later, typically during the twentieth century. For example, although the Bank of England was established in 1694, it did not behave as a lender of last resort until much later (Lovell 1957). Explicit government deposit insurance is an even later development than the lender-of-last-resort role of government central banks. In 1980, only 16 countries had explicit deposit insurance programs; by 1999, 68 countries had such programs (Garcia 1999; Demirgüç-Kunt and Sobaci 2000). Deposit insurance was adopted in the United States in 1934 and in Canada in 1967. In Germany, deposit insurance remains a private scheme, set up and run by the banks themselves. As with central banking generally, not only is deposit insurance late in developing, but also there is substantial cross-sectional variation as to whether it is private or public.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".