Using a Live Case Study and Co-opetition to Explore Sustainability and Ethics in a Classroom: Exporting Fresh Water to China
Bibliographic record
Abstract
The use of live case studies in business education is growing. Mixing realism entices students to think critically in an unpredictable environment. Live cases are often deemed appropriate for international business and strategic cases. This study reflects on an experience in which a live case study was used as a mechanism to invite students, unpredictably, to consider an ethical dilemma in international business. The live case incorporates the notion of sustainability, ethics and global business development. For one semester, a senior business course in international marketing was charged with the task of finding a strategy to export bottled water to China, from a Canadian source. In the process, some students won their way to China to assess how a strategy can be implemented first-hand. The experience shows that students were conflicted with the underlying principles of the mandate which involved exporting a natural resource abroad. Given that they were asked to share information with their competing colleagues (co-opetition), students themselves faced an ethical dilemma on a personal level. Some limitations and suggestions for future research are made.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".