The Holistic Stewardship Framework: Revolutionizing Management Education
Bibliographic record
Abstract
Universities and business schools are expected to play a crucial role in addressing modern challenges, given their key role in society and their vital function in generating and sharing knowledge. Traditional management education, often siloed and focused on economic feasibility, falls short in preparing leaders for today's complex landscape. This paper proposes the Holistic Stewardship Framework, a transformative approach to management education. Grounded in corporate sustainability, this paradigm adopts the United Nations Principles of Responsible Management Education (PRME) as its framework, with the United Nations Sustainable Development Goals (UNSDGs) serving as metrics for program and course outcomes. Our study examines teaching and learning practices, curriculum development, and policies worldwide, using secondary sources and publicly available data. The Holistic Stewardship Framework aligns with research emphasizing the role of responsible leadership in enhancing knowledge sharing and organizational performance. It addresses the need for interactive learning approaches fostering critical thinking and creativity. Practical examples demonstrate how integrating UNSDGs into courses equips future managers to navigate complexity, ethical dilemmas, and global challenges. This paradigm bridges theory and practice, cultivating leaders who balance economic viability with social responsibility and environmental stewardship. It represents a comprehensive shift in management education, preparing business leaders to thrive in a dynamic, interconnected world and address 21st-century challenges. Keywords: holistic stewardship framework, responsible management education, sustainable development goals, management education, responsible leadership
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".