Will Graduating Year Accountancy Students Cheat in Examination? : A Malaysian Case
Bibliographic record
Abstract
Due to a series of high profile accounting scandals and corporate collapses such as Enron, World.Com and Andersen, ethical conduct has been widely recognized as a crucial element in accounting profession and education. The growing concern over the ethics of professionals has also called for more academic research into this critical area. Our study aims at assessing ethical behaviors of the future accounting professionals (i.e. final year accounting students) in Malaysia. This study which uses questionnaire survey examined the students’ ethical attitudes as to whether they would act unethically in an examination. Also, their attitudes towards whistleblowing – if they become aware of such unethical conduct were examined. A vast majority of students (73 per cent), decided to be on the safe side – neither being purely unethical nor whistleblowers. Of the students, 11 per cent chose to become whistleblowers. While only 16 per cent would act unethically in exam, the percentage significantly declined once the risk of being caught was introduced. This indicates that students have not moved further from the first level of Kohlberg’s stages of moral development which highly depends on the punishment and penalty in order to behave ethically. Results also reveal that students with good academic achievement were less likely to cheat in exam. Furthermore, a larger proportion of male students as compared to female tend to behave unethically. Overall, the study indicates favorable results since the majority of respondents would not prefer to indulge in unethical behavior, although they are not being purely ethical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".