Education for sustainable development: Insights from Canadian and South African universities
Bibliographic record
Abstract
Universities can play a key role in contributing to a sustainable future for our planet and its inhabitants. In times of constant changes, there is a growing urgency to reflect on the vision of universities as well as their respective practices and projects that can promote the creation of sustainable societies. As is evident in scholarly literature, there is a need to empower universities and enhance their ability to prepare individuals who can confront global sustainability challenges and pursue sustainable development. The United Nations’ adoption of the 2030 Agenda for Sustainable Development in 2015 recognised that societal problems were territorially blind, meaning that no country has sufficient knowledge or research capacity to solve all challenges on its own. To that end, this qualitative, comparative research study represents the unified effort of two very different countries to explore the topic of education for sustainable development (SD) at universities. The study employed a document analysis of selected publicly funded universities in Canada and South Africa. Gathered documents are from the past seven years and include the universities’ mission and vision statements, annual reports, and strategic plans. The three main questions addressed in this work are: (a) What is the status and role of higher education for SD in Canada and South Africa? (b) What areas of SD are on the agenda of universities under investigation in Canada and South Africa? (c) What are the main similarities and differences between the two contexts under investigation? Findings indicate that universities focus on several aspects of SD, namely sustainable education, sustainable relationships, and sustainable initiatives. The paper discusses these areas for each country in connection to their contextual setting. Although the study’s findings cannot be generalised, they can be informative for other universities and contexts and thus contribute to the body of knowledge about education for SD in higher education.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".