Productivity in “Top‐Ten” Academic Accounting Journals by Researchers at Canadian Universities*
Bibliographic record
Abstract
ABSTRACT We examine the research productivity of academic accountants at Canadian universities for the 11‐year period 1990‐2000. Our analysis is based on the “top‐ten” ranked refereed journals in accounting, auditing, and taxation, as documented by Brown and Huefner (1994). We first provide an overview of the importance of publishing in highly ranked accounting journals for individual academics, departments, and business faculties. We then provide details of the proportion of articles published in each of these journals by academics from Canadian universities; the type of research published in each journal (auditing, financial accounting, managerial accounting, and taxation); and details of editorial board service. Our results indicate that even at the most productive Canadian university (in terms of “top‐ten” publications), faculty members publish (on average) approximately one article every seven years. Six Canadian universities have faculty members with, on average, more than one article in “top‐ten” journals every 10 years. We also provide results of analyses that rank each Canadian university, after controlling for the relative quality of each journal, using impact factors published by the Social Science Citation Index. In addition, statistics are provided with regard to the 15 most productive researchers, in terms of “top‐ten” publications, in the 11‐year period. Finally, in conjunction with the 25th anniversary of the Canadian Academic Accounting Association, we examine the productivity of academic accountants at Canadian universities over the past 25 years by combining our results with those reported by Richardson and Williams (1990).
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.043 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".