Context of Vignettes and Ethical Sensitivity in Decision-Making Among Undergraduate Business Studies Learners at University of Nairobi, Kenya
Bibliographic record
Abstract
Vignettes have been applied to train professionals in various fields, which has contributed to significant improvements in learning outcomes, ethical sensitivity and learners’ ethical decision-making. At the University of Nairobi’s Department of Educational Communication and Technology, most instructors have been slow to embrace experiential learning and inconsistent in applying vignettes to deliver business ethics lessons that emphasise ethical sensitivity in decision-making, with far reaching effects on the quality of graduates. This study responded to the information gap about the relationship between the use of vignettes and learners’ ethical sensitivity in decision-making at the University of Nairobi. Cross-sectional survey design guided the research process, and primary data were sourced in 2018 from 116 learners. Both quantitative and qualitative analysis techniques were applied. Key results show that learners who agreed strongly that the context of vignettes influences ethical sensitivity in decision-making were about 3.9 times as likely to make ethically sensitive decisions as colleagues who disagreed strongly. Those who agreed that the context of vignettes influences ethical sensitivity in decision-making had about 2.3 times the odds of making ethically sensitive decisions as colleagues who indicted strong disagreement. This means that the more the learners appreciated that the context of vignettes influences ethical sensitivity in decision-making, the higher the chances of them making ethically sensitive decisions, and vice-versa. This brings to the fore the need for instructors to consistently apply the context of vignettes to deliver business ethics lessons to improve learners’ ethical sensitivity and propensity to make ethical decisions. The study recommends that for practice, vignettes business contexts should be integrated in teacher training business studies ethics lessons.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".