Productivity in Top‐10 Academic Accounting Journals by Researchers at Canadian Universities at the Start of the 21st Century
Bibliographic record
Abstract
Abstract We assess the research publication productivity of Canadian‐based accounting researchers in highly ranked accounting journals for the 2001–13 period. Our research provides important benchmarks for use by individual researchers and universities for matters such as promotion and tenure decisions. For example, each Canadian‐based faculty member had approximately 0.50 of a weighted article for the 13‐year period, and 45 percent of all accounting faculty members published at least once in a top‐10 accounting journal. We also provide an overview of the type of research being published by Canadian‐based researchers in each of the top‐10 journals (financial accounting, managerial, audit, tax or other) and we assess how productivity at top‐10 journals has changed over time. In supplemental analysis, we compare and contrast the productivity of the 15 male and 15 female academics that publish most in top‐10 accounting journals to assess the breadth of outlets being used beyond top‐10 outlets (including FT 45 journals, accounting journals ranked “A”, “B”, and “non‐A/B”; non‐accounting peer‐reviewed journals, non‐peer‐reviewed outlets). The supplemental analysis also helps to shed light on the finding from this paper, and prior research, that women are less likely to be represented on lists of those with most publications in highly ranked accounting journals, by comparing the two groups of researchers across a variety of institutional and other factors.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".