Bibliographic record
Abstract
Gold rushes are periods of economic boom, generally associated with large increases in expenditures aimed at securing claims near new found veins of gold.An interesting aspect of gold rushes is that, from a social point of view, much of the increased activity is wasteful since it contributes simply to the expansion of the stock of money.In this paper, we explore whether business cycle fluctuations may sometimes be driven by a phenomenon akin to a gold rush.In particular, we present a model where the opening of new market opportunities causes an economic expansion by favoring competition for market share, which is essentially a dissolution of rents.We call such an episode a market rush.We construct a simple model of a market rush that can be embedded into an otherwise standard Dynamic General Equilibrium model, and show how market rushes can help explain important features of the data.We use a simulated-moment estimator to quantify the role of market rushes in fluctuations.We find that market rushes may account for over half the short run volatility in hours worked and a third of the short run volatility of output.
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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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".