Organizational Learning Towards Sustainability in Higher Education Institutions: A Brazilian Case Study
Bibliographic record
Abstract
Universities should be dedicated to providing an education that fosters social transformation by interrelating the environmental, social, and economic dimensions. This article asserts that a truly sustainable university extends beyond merely “greening” the campus—it emphasizes sustainability across all core processes, be it in education, management, research, or community relations. The primary aim of this study is to delve into organizational learning and sustainable management processes, drawing inspiration from the I3E model introduced by Cebrian (2016). A qualitative case study was undertaken at a Brazilian federal university listed in the UI GreenMetric World University Rankings to achieve this. The research thoroughly examined institutional documents and interviews with managerial staff to discern strategic decisions, sustainable initiatives, and best practices. The findings suggest that the journey towards learning for sustainability is riddled with challenges, such as a paucity of commitment, communication gaps, ineffective leadership, distrust within the institution, limited funding, conflicting interests among various groups and individuals, and the burden of rigid bureaucratic protocols. However, facilitating factors have also been identified, including reshaping pre-existing mental models, a genuine interest in learning, and a conducive organizational framework. This research enriches the existing discourse on sustainability and organizational learning and offers a comprehensive and interconnected perspective on these topics. Additionally, it delivers valuable insights for academic administrators aspiring to craft a blueprint for a sustainable university. It does so by addressing overarching challenges and placing the components of the institutional management model in context, thereby offering direction for sustainable core processes in areas such as teaching, research, outreach, and management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".