Bibliographic record
Abstract
We construct a random matching model of a monetary economy with commodity money in the form of potentially different types of silver coins that are distinguishable by the quantity of metal they contain. The quantity of silver in the economy is assumed to be fixed, but agents can mint and melt coins. Coins yield no utility, but can be traded. Uncoined silver yields direct utility to the holder. We find that optimal coin size increases with the probability of trade and with the stock of silver. We use these predictions of our model to analyze the coinage decisions of the monetary authorities in medieval Venice and England. Our model provides theoretical support for the view that decisions about coin sizes and types during the medieval period reflected a desire to improve the economic welfare of the general population, not just the desire for seigniorage revenue. The figures are best viewed in color. We thank Miguel Molico, François Velde, Randy Wright, and participants at seminars at the Federal Reserve Bank of Boston, the University of British Columbia, Yale University and the University of Oxford for helpful comments. Redish gratefully acknowledges the support of the Social Sciences and Humanities Research Council of Canada. The views expressed in this paper are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System. Any study of the money supply [of medieval Europe] needs to take account not only of
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".