Sustainability Centres and Fit: How Centres Work to Integrate Sustainability Within Business Schools
Bibliographic record
Abstract
For nearly as long as the topic of sustainable business has been taught and researched in business schools, proponents have warned about barriers to genuine integration in business school practices. This article examines how academic sustainability centres try to overcome barriers to integration by achieving technical, cultural and political fit with their environment (Ansari et al. in Acad Manag Rev 35(1):67–92; Ansari et al., Academy of Management Review 35(1):67–92, 2010). Based on survey and interview data, we theorise that technical, cultural and political fit are intricately related, and that these interrelations involve legitimacy, resources and collaboration effects. Our findings about sustainability centres offer novel insights on integrating sustainable business education given the interrelated nature of different types of fit and misfit. We further contribute to the literature on fit by highlighting that incompatibility between strategies to achieve different types of fit may act as a source of dynamism.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.005 | 0.004 |
| 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".