Ranking North American Accounting Scholars Publishing Education Papers: 1966 Through 2011
Bibliographic record
Abstract
This paper ranks accounting’s education authors who teach at institutions located in the United States and Canada. During the 46-year period from 1966 through 2011 that we examined, 13 journals published accounting education papers; the publication period for each journal varies. The data indicate that only 31.4 percent of accounting’s 4,855 doctoral faculty, who teach at schools in North America, have one or more publications in these 13 journals. For those doctorates still teaching, the research provides rankings of authors by doctoral year and for four periods: 2002 to 2011 (most recent 10 years), 1992 to 2001 (next 10-year period), 1966 to 1991 (last 26 years), and for the entire 46-year period. To acknowledge the contributions of retired and deceased authors, the research lists those authors who would have been included on the overall list had they still been actively teaching. While Urbancic (2009) and Brigham Young University (BYU) provide rankings of authors in accounting education, these rankings are limited in the scope of the journals included – Urbancic includes only six accounting education journals, while BYU includes only Issues in Accounting Education. We found that Urbancic’s (BYU’s) 10-year (20-year) data had a Spearman’s rho of -0.84 (0.39) with our rankings. We believe that data provides a more comprehensive ranking of accounting’s authors in the area of education.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.082 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".