Bibliographic record
Abstract
Benjamin Williams of George Washington University reviews “Structural Econometric Models”, by Eugene Choo and Matthew Shum. The Econlit abstract of this book begins: “Twelve papers explore recent developments in the use of structural econometric models in empirical economics. Papers discuss Euler equations for the estimation of dynamic discrete choice structural models; approximating high-dimensional dynamic models—sieve value function iteration; identifying dynamic games with serially correlated unobservables; partial identification in two-sided matching models; identification of matching complementarities—a geometric viewpoint; comparative static and computational methods for an empirical one-to-one transferable utility matching model; a test for monotone comparative statics; estimating supermodular games using rationalizable strategies; estimation of the loan spread equation with endogenous bank-firm matching; the collective marriage matching model— identification, estimation, and testing; deflation in durable goods markets—an empirical model of the Tokyo condominium market; and a dynamic analysis of the U.S. cigarette market and antismoking policies. Choo is with the Department of Economics at the University of Calgary. Shum is with the Division of Humanities and Social Sciences at the California Institute of Technology.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".