Bibliographic record
Abstract
Purpose This paper aims to make suggestions for addressing the apparent failure of business schools to communicate good ethical decision-making skills to students studying in postsecondary institutions. Design/methodology/approach At the beginning of university management classes, a form is distributed to students outlining a short scenario that requires a course of action. The two questions asked, based on this decision context, are (i) is it ethical to offer bribes to secure construction contracts in countries where this practice is considered an acceptable way of conducting business and (ii) has the student completed an ethics course at the university? Findings The scenario described is what occurred at SNC Lavalin, a Canadian construction company that was charged and convicted with offering illegal inducements to foreign officials (in Libya) to secure large government construction contracts. Ethically, the “right” decision would be to not offer bribes; this is because they are illegal when offered both in the Canada and in foreign jurisdictions. The response results to the survey questionnaire showed that 21% of the students thought that bribery was acceptable, if it was a customary business practice in the country where the transaction occurred, and 93% of these students had taken an ethics course. It was interesting to note that almost all the students, who had not taken a business ethics courses, thought that bribery is not acceptable under the circumstances described. Originality/value To address the apparent failure to communicate good ethical decision-making skills, this essay suggests that when teaching business ethics, there should a clearer focus on (i) the distinction between morality and ethics; (ii) the problem posed by relativism; and (iii) the reasoning behind ethical standards. This approach is novel in that it makes sense from the perspective of both a business practitioner and university educator.
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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".