Reviews and developments in accounting education literature: A longitudinal analysis of specialized journals
Bibliographic record
Abstract
This paper provides a comprehensive analysis of accounting education research, drawing on 19 review articles (1986–2023) and a quantitative assessment of studies published in specialized journals (1997–2024). Methodologically, the research integrates qualitative and quantitative approaches, covering the widest time span examined to date. The volume of articles has fluctuated, with peaks (2006–2009, 2022–2024) and troughs (2013–2015, 2019–2021). Key themes include instruction in specific content areas, student issues, faculty, and the teaching/assessment process, with empirical and descriptive studies being evenly balanced. The main data source continues to be the United States, Canada, and Mexico, while contributions from Europe, Australia–New Zealand, and emerging regions (Asia, Africa, the Middle East) have progressively increased. The conclusions underscore the importance of rigorous methodologies, the integration of educational technologies, and the expansion of both geographical and interdisciplinary contexts. The paper also recommends placing greater emphasis on diversity and inclusion and aligning the curriculum with the changing needs of the accounting profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".