Drivers and barriers to sustainability in higher education institutions (HEIs): a case study from the global south
Bibliographic record
Abstract
Against the backdrop of the United Nations Sustainable Development Goals (UN-SDGs), our understanding, interest and uptake of sustainability and sustainable business practices continues to gather pace. Whilst a little further behind other sectors, higher education institutions (HEIs) are beginning to explicitly demonstrate their commitment to societal improvement by embedding sustainability throughout their education portfolio, research, governance structures, operations and community outreach practices (Sterling, 2004, Lozano et al., 2013). As a result, there is an emerging literature that explores the role, impact, opportunities and challenges of embedding sustainability inHEIs. Scholars have identified a range of internal and external drivers that facilitate this ‘journey’ whilst also noting some of the inherent barriers that are stumbling blocks to integrating sustainability in HEIs(Lozano, 2006, Ferrer-Balas et al., 2009; Blanco-Portela et al, 2017). One of the most cited approaches to addressing the challenges and integrating sustainable practices is to focus on stakeholder management and engagement (as suggested by Barth, 2013, Leal Filho et al., 2022, D’Adamo and Gastaldi, 2023).Such is the impact of stakeholders, that Blanco-Portela et al. (2017) suggest that they play a double role as both the solution and the barrier to sustainability transition. To date, most of the empirical research focusing on sustainability in HEIs has centred on the global north. Yet, anecdotally, there is growing evidence that HEIs elsewhere, and, in particular, in the global south are also making concerted efforts in relation to sustainability. As Leal Filho et al (2022) and Weiss and Barth (2019) remind us,sustainability represents a critical need in a developing country context and the insights offered by HEIs in the global south represent an important extension of the current literature. This paper investigates the drivers and barriers of incorporating sustainability throughout the core business of HEIs in the global south, focusing on Saudi Arabia. Through a case study approach, the research tries to build a better understanding as to why HEIs are motivated to become a sustainable, how they deal with potential and actual barriers and the overall impact and ‘success’ of their sustainability efforts.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".