From the Classroom to Journal Publication: A Guide to Publishing Accounting Instructional Pedagogical Resources
Bibliographic record
Abstract
ABSTRACT Calls for increased rigor in accounting education persist, but it is unclear if the field has progressed. Assessing peer-reviewed accounting instructional pedagogical resources (i.e., instructional resources and cases) is crucial to fostering more rigorous contributions. To understand how academics can make such contributions, we analyzed instructional resources and cases with thematic analysis and machine learning methods. Predominant technical competencies were auditing, information technology, and financial accounting, whereas the most prevalent themes were data analytics in managerial accounting, profit and cost analysis of organizations, financial statements, and analytics and the auditor’s role. Underrepresented areas include financial accounting and reporting subtopics (e.g., nonprofit, derivatives, hedging instruments), technology subtopics (e.g., privacy-enhancing computation, cloud-native platforms, decision intelligence, generative AI), and areas of societal importance (e.g., imperialism, queering, equity, indigeneity). Encouraging academics to publish instructional pedagogical resources on these subjects could lead to innovative articles and address the need for increased rigor in accounting education. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G33; M21.
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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 | 0.030 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.092 |
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".