Viewpoint: A microfoundation of
Bibliographic record
Abstract
Abstract. In this lecture, I explain what the microfoundations of money are about and why they are necessary for monetary economics. Then, I review recent developments of a particular microfoundation of money, commonly known as the search theory of money. Finally, I outline some unresolved issues. JEL classification: E40, E50 Une microfondation de l’économie monétaire. Dans cette présentation, l’auteur explique ce que sont les microfondations de la monnaie, et pourquoi elles sont nécessaires a ̀ l’économie monétaire. Ensuite, il passe en revue certains développements récents d’une microfonda-tion particulière de la monnaie connue sous le nom de théorie de la recherche de la monnaie. Enfin, l’auteur fait le tour d’un ensemble de problèmes qui restent sans solution pour le moment. 1. Monetary issues In this paper, I review the recent development of a microfoundation of monetary economics, known as the search theory of money. To explain what a microfoun-dation of monetary economics is about and why it is necessary, let me list the main issues in monetary economics as follows. (I1) Existence and essentiality of fiat money. Why would intrinsically worthless money have value? How can fiat money improve the efficiency of resource allocations? This paper is based on a lecture given at the Canadian Economics Association Meetings (Hamilton 2005). I am grateful to Angelo Melino, Steve Williamson, and an anonymous referee for valuable comments, and to Hilary Shi for editorial assistance. I gratefully acknowledge financial support from the Bank of Canada Fellowship and from the Social Sciences and
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".