Bibliographic record
Abstract
This paper provides a tractable search model with divisible money that encompasses the two frameworks currently used in the literature. In the model, individuals belong to many villages. Inside a village, individuals are not altruistic as in a representative household, but they share information so financial contracts are feasible. Money is essential in the model to facilitate trade with individuals outside the village. The framework proposed by Lagos and Wright (2002) arises as a special case if some goods trade in competitive markets while others trade in search markets, and preferences are quasi-linear. The framework proposed by Shi (1997) arises as a special case if individuals can insure trading risks inside the village. In general, if preferences are not quasi-linear and trading risks cannot be insured, the distribution of money holdings is non-degenerate and monetary transfers have distributional effects. However, neither quasi-linear preferences nor insurance of trading risks are necessary for tractability. Indeed, this paper advances a tractable benchmark with an endogenous frequency of shopping in which all buyers choose to carry the same amount of money even if preferences are not quasi-linear and trading risks cannot be insured
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".