Bibliographic record
Abstract
Over the last decade, numerous modules, courses, and programs in Management Education have integrated sustainability into their curricula. However, this “integration” has translated into very diverse forms and contents. This article aims to clarify these ambiguities. It maps four forms of sustainability integration in Management Education. These four distinct forms are (1) discipline-based integration, in which the anchoring point is the business discipline (sustainability is added as a dimension of this body of knowledge); (2) strategic-/competitive-based integration, in which the anchoring point is the strategy of the organization (sustainability is viewed as a potential contributor to the firm’s competitive advantage); (3) integration by application, in which managerial tools and approaches from business disciplines are applied so as to contribute to addressing a sustainability challenge; and, last, (4) systemic integration, in which the anchoring point is a social-ecological-economic challenge defined from an interdisciplinary perspective. Implications of this chapter for the design of courses and programs and the practice of sustainability in Management Education are twofold. First, this article contributes to going beyond the prevailing tendency of studies in the field of sustainability in Management Education to focus mainly on tools and applications. In doing so, this article helps frame these challenges on the level of course and program design. Second, this article helps management educators map what they are intending to achieve by the integration of sustainability into the Management Education curriculum.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".