Ranking Accounting Authors and Departments in Accounting Education: Different Methodologies – Significantly Different Results
Bibliographic record
Abstract
This research tests whether Holderness Jr., D. K., Myers, N., Summers, S. L., & Wood, D. A. [(2014). Accounting education research: Ranking institutions and individual scholars. Issues in Accounting Education, 29(1), 87–115] accounting-education rankings are sensitive to a change in the set of journals used. It provides updated rankings for accounting-education authors from Australia, Canada, New Zealand, the Republic of Ireland, the United Kingdom, and the United States using a sample that included the publications in 13 accounting-education journals. Our analysis indicated that Holderness et al.’s rankings of authors and departments were significantly different from our rankings. This research provides rankings of the top 50 authors and departments for three periods: from 2010 to 2015, from 2004 to 2015, and from 1992 to 2015. We provide data indicating the distribution of authors for these periods to assist authors not listed in the most prolific lists in determining their relative ranking. Finally, we provide data on the distribution of journal choices for accounting-education publications for the authors from each country.
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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 | 0.027 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".