Interdisciplinarity-Based Sustainability Framework for Management Education
Bibliographic record
Abstract
Business education faces shortcomings that can be mitigated through the broad perspective of interdisciplinarity, fulfilling a call for a greater orientation toward Education for Sustainable Development (ESD). Despite the relevance and urgency, current frameworks cannot embed context-related problems into their design, increasing the detachment of wicked problems and management education, and falling short of the goal-oriented prerogative. Interdisciplinarity is up to this task as an educational attitude and behaviour rather than a toolkit of cross-disciplinary classification. This paper aims to propose a framework for interdisciplinarity-based sustainability management for business education. We established the framework via a literature review analysis, and then we validated it through discussions with specialists from the United Nations Principles for Responsible Management Education (UN-PRME) to introduce a model with 49 evidence-driven, interdisciplinarity practices. We grouped results in three main dimensions of analysis connecting the 16 categories. We gave special attention to spaces of discomfort that ought to be fostered in business schools under a critical thinking perspective and the student’s role in the relevance of sustainability education. The work harbours practical implications for developing better practices for management education by blending an interdisciplinary approach to sustainability in the management education literature.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".