Bibliographic record
Abstract
The Journal of Accounting Research is proud to recognize our top referees of the previous calendar year. The senior editors selected those named below for their “2022 Excellence in Refereeing” based on the quality and the number of reviews they had performed for the journal during the 2022 calendar year. We thank the referees for their invaluable services to the journal. Jannis Bischof, University of Mannheim Matthew Bloomfield, University of Pennsylvania Pietro Bonetti, IESE Business School Thomas Bourveau, Columbia University Mark Bradshaw, Boston College Matthias Breuer, Columbia University Jung Ho Choi, Stanford University Yiwei Dou, New York University Raphael Duguay, Yale University Travis Dyer, Brigham Young University Henry Eyring, Duke University Fabrizio Ferri, University of Miami Henry Friedman, University of California, Los Angeles Stephen Glaeser, University of North Carolina João Granja, University of Chicago Nicholas Guest, Cornell University Steven Kachelmeier, University of Texas, Austin John Kepler, Stanford University Sehwa Kim, Columbia University Ranjani Krishnan, Michigan State University Lian Fen Lee, Boston College Miao Liu, Boston College Yao Lu, Cornell University Daniele Macciocchi, University of Miami Charles McClure, University of Chicago Mihir Mehta, University of Michigan Maximilian Muhn, University of Chicago James Omartian, University of Michigan Gaizka Ormazabal, IESE Business School Hong Qu, Kennesaw State University Thomas Rauter, University of Chicago Delphine Samuels, University of Chicago Timothy Shields, Chapman University Nemit Shroff, MIT Lorien Stice-Lawrence, University of Southern California Stephen Stubben, University of Utah Andrew Sutherland, MIT Sorabh Tomar, Southern Methodist University Rahul Vashishtha, Duke University Felix Vetter, University of Mannheim Dushyantkumar Vyas, University of Toronto Charles Wang, Harvard Business School Clare Wang, University of Colorado, Boulder Edward Watts, Yale University TJ Wong, University of Southern California Gaoqing Zhang, University of Minnesota Frank Zhou, University of Pennsylvania Christina Zhu, University of Pennsylvania Luo Zuo, Cornell University
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 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 | 0.098 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.037 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.059 |
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".