Bibliographic record
Abstract
Citation (2014), "List of Contributors", Bayesian Model Comparison (Advances in Econometrics, Vol. 34), Emerald Group Publishing Limited, Bingley, pp. vii-viii. https://doi.org/10.1108/S0731-905320140000034015 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Daniel Felix Ahelegbey Graduate School of Economics and Management, University of Venice, Venice, Italy Miguel Belmonte Department of Economics, University of Strathclyde, Glasgow, UK Gail Blattenberger Department of Economics, University of Utah, Salt Lake City, UT, USA Martin Burda Department of Economics, University of Toronto, Toronto, Canada Garland Durham Orfalea College of Business, California State Polytechnic Institute, San Luis Obispo, CA, USA Richard Fowles Department of Economics, University of Utah, Salt Lake City, UT, USA John Geweke School of Business, University of Technology Sydney, Sydney, Australia; Erasmus University, Rotterdam, The Netherlands; Colorado State University, Fort Collins, CO, USA Benjamin J. Gillen Department of Economics, California Institute of Technology, Pasadena, CA, USA Paolo Giudici Department of Economics and Management, University of Pavia, Pavia, Italy Esther Hee Lee EViews, IHS Global Inc., Irvine, CA, USA Ivan Jeliazkov Department of Economics, University of California – Irvine, Irvine, CA, USA Gary Koop Department of Economics, University of Strathclyde, Glasgow, UK Peter D. Loeb Department of Economics, Rutgers University, Newark, NJ, USA Enrique Martínez-García Research Department, Federal Reserve Bank of Dallas, Dallas, TX, USA Elías Moreno Department of Statistics, University of Granada, Granada, Spain Luís Raúl Pericchi Department of Mathematics, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico, USA Dale J. Poirier Department of Economics, University of California – Irvine, Irvine, CA, USA Hyungsik Roger Moon Department of Economics, University of Southern California, Los Angeles, CA, USA Matthew Shum Department of Economics, California Institute of Technology, Pasadena, CA, USA Angela Vossmeyer Department of Economics, University of California – Irvine, Irvine, CA, USA Guillaume Weisang Graduate School of Management, Clark University, Worcester, MA, USA Mark A. Wynne Research Department, Federal Reserve Bank of Dallas, Dallas, TX, USA Book Chapters Bayesian Model Comparison Advances in Econometrics Bayesian Model Comparison Copyright Page List of Contributors Preface Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments Model Switching and Model Averaging in Time-Varying Parameter Regression Models Assessing Bayesian Model Comparison in Small Samples Bayesian Selection of Systemic Risk Networks Parallel Constrained Hamiltonian Monte Carlo for BEKK Model Comparison Factor Selection in Dynamic Hedge Fund Replication Models: A Bayesian Approach Determining the Proper Specification for Endogenous Covariates in Discrete Data Settings Variable Selection in Bayesian Models: Using Parameter Estimation and Non Parameter Estimation Methods Intrinsic Priors for Objective Bayesian Model Selection Demand Estimation with High-Dimensional Product Characteristics Copula Analysis of Correlated Counts
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.731 | 0.743 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".