Engaging Staff in Curriculum Change: Reflections from an Accounting Ethics Initiative
Bibliographic record
Abstract
This paper identifies the challenges associated with engaging staff in curriculum change, using the context of systematic inclusion of ethics in the accounting curriculum of a major Australian metropolitan university, and offers some suggestions as to how these challenges might be overcome. We characterize the inclusion of ethics in the accounting curriculum as ‘pluri-disciplinary’ following the typology of Davies and Devlin (2007) and draw on 22 interviews with accounting academics to examine curriculum change in a pluri-disciplinary context. We find that key staff concerns are the impact on broader accounting discourse, assignment of teaching responsibilities, curriculum content, and identification of who is ultimately responsible for the curriculum change. The responses indicate that staff would like to be equipped to confidently deliver ethics content and to have material relevant to a technically-focused student cohort. One means of achieving this might be to involve ethics experts in developing and delivering foundational material early in the curriculum and having accounting staff teach applications of this material in the latter stages. Our observations might also be of interest to those seeking to embed other ‘soft’ skills (such as communication, critical thinking and sustainability) within a technical curriculum.
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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 | 0.039 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".