Sustainability practices at higher education institutions in Asia
Bibliographic record
Abstract
Purpose It is still unclear how Asian universities incorporate the theory or practice of sustainable development (SD) in their research and education programmes. To address this gap, the purpose of this paper is to report on a study that has examined how universities in Asian countries handle and address matters related to SD. Design/methodology/approach The study used a bibliometric analysis and an online survey-method. The online survey data were analysed through descriptive analysis and one-sample student’s t -test. Findings The study indicates that there is considerable variation among the Asian countries regarding sustainability practices in higher education institutions (HEIs). The HEIs in far eastern countries, such as Indonesia, Malaysia and Thailand are perceived to demonstrate more sustainability practices. Research limitations/implications Even though a substantial number of participants participated in the survey, it did not cover all Asian countries. The online survey was carried out over a limited period of time, and not all HEIs in the field may have received information about the study. Practical implications Asia is the largest continent facing a number of sustainability challenges. In this context, the contribution of HEIs is very important. The findings of the current study may serve as a baseline for Asian HEIs to take more initiatives towards SD goals, as HEIs are responsible for the education and training of hundreds of thousands of students who will be occupying key positions in industry, government or education in the coming years. Originality/value The study contributes to the existing literature in two distinct ways. First, it was possible to develop a comprehensive instrument to measure sustainability practices in HEIs. Second, this study has filled the gap of the scarcity of studies regarding sustainability practices in HEIs in Asia.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".