Accounting-education trends by authors from Australia, Canada, New Zealand and the United Kingdom
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the trends in accounting-education publications and the influence of journal rankings for authors from Australia, Canada, New Zealand and the UK. Design/methodology/approach – The authors included the publications in ten accounting-education journals for the 20-year period from 1993 to 2012. Findings – The data provide insights into the perceptions of accounting-education journals by authors from four countries. The authors found that, while the use of Accounting Education as a publication outlet for accounting authors from Australia and the UK was relatively stable, the use of Accounting Education as a publication outlet increased (decreased) for the accounting authors from New Zealand (Canada). The authors also found that, while coauthoring by the accounting authors from Australia and the UK increased slightly, coauthoring by the accounting authors from Canada and New Zealand increased during the 20-year period. Research limitations/implications – The data suggests a tendency by the authors from these four countries to publish their accounting-education research in journals that had been ranked as a top accounting journal. Originality/value – This paper is the first paper to consider trends in international accounting-education publications. The data in this research can be used by accounting faculty wishing to assess which journals their colleagues publish in most frequently.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".