Do Accounting Academics Have the Expertise to Teach a Discipline-Specific Ethics Course? A Research Assessment Approach
Bibliographic record
Abstract
Ethics is of increasing concern to many important stakeholders in accounting education. While the Association to Advance Collegiate Schools of Business (AACSB) encourages institutions to demonstrate their commitment to ethics through research agendas, the National Association of State Boards of Accountancy proposed a three-course ethics sequence - one of which was a discipline-specific accounting ethics course. The unanswered question is whether or not the accounting academy has the embedded expertise in ethics to teach a discipline-specific accounting ethics course. While there are numerous ways to establish an expertise in the area of ethics, this study documents the level of ethics research by accounting faculty who teach at institutions in North America in 26 business ethics journals and accounting's Top-40 journals. Our data indicate that 683 (546) schools or 75.9 (60.6) percent of the 900 schools in the United States and Canada do not have an ethics scholar if one uses a five-year (20-year) research window. The study establishes a baseline that allows stakeholders to assess the level of past, present and future accounting ethics research, and the capabilities of accounting faculty to address alternative ethics education pedagogies. Copyright © 2008 by Emerald Group Publishing Limited All rights.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".