Bibliographic record
Abstract
Comments focused on modeling choice, Bayesian techniques, and interpretation of the results.Andrew Levin was surprised the authors chose to apply their model to the United States and Europe.To Levin, a more reasonable starting point for Bayesian estimation of new open-economy macroeconomic models is the classic case of a small open economy, or if the goal is to explore the interactions between two economies, Levin suggested starting with country pairs that do a substantial amount of trade with each other, such as the United States and Canada, or the Euro area and the United Kingdom.By contrast, the United States and the Euro area represent two economies that are relatively closed [trade is only about 10 percent of gross domestic product (GDP), only 10 percent of their trade is with each other, U.S. exports to the Euro area account for only 1 percent of U.S. GDP], and therefore it takes very large shocks to the Euro area before they show up in direct standard trade linkages.Levin was also concerned about a model that assumes a single monetary policy in each economy being fit to European data from the 1970s and 1980s.In terms of Bayesian techniques, Ken Rogoff was curious about the authors' claim that Bayesian papers are easier to communicate to policymakers.Schorfheide acknowledged the challenge of communicating what exactly is learned from the data and how the prior distributions affect the conclusions that are ultimately reached.But he maintained that one can nicely capture the effects of uncertainty on the decision, and therefore the researcher can incorporate uncertainty into the decision-making process.Chris Sims also was not so sure that it is easier to communicate Bayesian results.But he agreed with Schorfheide that Bayesian methodsin principle-allow one to combine the policymaker's subjective information with the model.According to Sims, the formal econometrics
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".