Unlocking the potential of responsible management education through interdisciplinary approaches
Bibliographic record
Abstract
Abstract Business schools are crucial to integrating sustainable development into management thought and practices, thereby promoting a paradigm shift toward responsible management education. Despite many business schools pledging to adopt the United Nations' Principles for Responsible Management Education, they have been criticized for failing to develop change agents toward sustainability. To fill this gap, this paper demonstrates how interdisciplinarity can be connected to responsible management education through critical and instrumental perspectives. To this end, we apply an interdisciplinarity model to 37 Principles for Responsible Management Education Schools' Reports, using content analysis, text‐mining, and network theory tools. As a result, our findings suggest: (i) a taxonomy of critical and instrumental interdisciplinary studies and (ii) a framework of Principles for Responsible Management Education schools engaged in critical and instrumental interdisciplinarity. The framework we develop can serve as a diagnostic and prognostic tool for assessing how interdisciplinary can improve responsible management education in business schools. Our findings contribute to theory advancing research on the intersection of responsible management education and interdisciplinary approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".