Educating for Sustainability: The Crucial Role of the Tertiary Sector
Bibliographic record
Abstract
The Sustainable Development Goals of the United Nations represent a universal response to current global challenges that include climate change, poverty, political instability and the massive displacement of people worldwide. The central role of education in achieving sustainable development has been internationally acknowledged and successfully promoted: Global enrolment rates are now 90 percent for primary education and over 70 percent for secondary education. Building on these achievements, this paper focuses the role of tertiary education in contributing to sustainable development. This study reviewed recent theoretical and empirical research relating to the field. Conclusions from theoretical studies confirm that building on human capital is crucial for achieving the sustainable development goals. The majority of empirical studies also confirm a positive correlation between tertiary education and sustainable development. This study highlighted, however, that the full benefits of tertiary education to society may have been underestimated and that there are significant research gaps in the field. Furthermore, current challenges including funding, equity and market relevancy in tertiary education need to be addressed. Given the pressing global issues and the mounting evidence of positive impacts, this paper calls for more research and attention to be devoted to tertiary education in the sustainable development debate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".