GOING OUTSIDE THE CLASSROOM: ALTERNATIVE WAYS OF INTEGRATING ETHICS AND SOCIAL IMPACT MANAGEMENT INTO BUSINESS EDUCATION
Bibliographic record
Abstract
We consider the potential of service learning and outside of the classroom initiatives such as student interest groups to bring ethics, social impact management, corporate social responsibility and environmental awareness into business education. The proliferation of recent business scandals have the public, business practitioners and academics attentive to the integration of ethics and moral value judgments within the decision making process in business practices (Ahmed, Chung and Eichenseher, 2003). The students currently attending business schools are the world’s future business leaders and they need to be provided with the tools that will help them to not only survive in the business world, but also to help society survive in the business world. In this paper, we consider the potential of ‘beyond Business Ethics class ’ initiatives such as service learning and student interest groups that can assist business schools in integrating topics such as business ethics, social impact management, corporate social responsibility and environmental awareness into the education process. 6 In addition, we gathered data on established student clubs, societies and groups that focus on social impact management and are connected to Canadian business schools. In particular, we focused on one Atlantic Canadian university to consider student awareness and interest in integrating
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".