Exploring Reflections of Lecturers on Ethics in the Accounting Curriculum: A Case Study of Ukzn and Unizulu, South Africa
Bibliographic record
Abstract
The accounting profession has changed considerably over the past twenty years or so. This has led to the new accounting curriculum undergoing many changes such as increased financial fraud coverage, laws relating to financial crime and globalisation and financial security issues. However, there have been many financial scandals in the 21st century in the African and American context. Several notable incidents of ethical misconduct such as Enron, Klynveld Peat Marwick Goerdeler (KPMG), and Satyam (which negatively impact the world economy) are examples of misuse of accounting to cover the truth. Lack of ethics is believed to be responsible for most of these scandals. This study, therefore, aims at understanding the reflections on the issue of ethics in teaching accounting. The study followed a case study approach and involved a small sample of accounting lecturers from UKZN and UNIZULU. The study's findings suggest that special topical issues on ethics should be taught in each accounting course. For example, in the auditing course, ethical procedures should be touched upon, and students should be sensitized about accountants' ethical behaviour.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".