Investigating the Critical Issues for Enhancing Sustainability in Higher Education Institutes in Thailand
Bibliographic record
Abstract
Higher Education Institutions (HEIs), by and large, have increasingly committed to integrate sustainable development (SD) into their policies, practices, and programs. Recently, there have been several sustainability assessment tools specifically developed for HEIs. Many HEIs, especially small-to-middle sized HEIs in Thailand, are planning to enhance SD but are reluctant due to resource requirements. This study was conducted to investigate important sustainability implementation issues, including the effect of HEI sizes and UI GreenMetric participation. A weighting approach on sustainability dimensions and issues was utilized, and HEI’s sustainability reports and official websites were reviewed to evaluate their sustainability performance of large-, middle-, and small-sized HEIs as well as UI GreenMetric participants and non-participants in Thailand. The findings reveal that the issues of the sustainability-integrated vision and strategy, safety and well-being, waste, and the SD-enhancing educational system were fundamentally critical for HEI sustainability. Moreover, most of the large-sized HEIs in Thailand that participated in UI GreenMetric were evaluated to have higher sustainability performance than others, apparently in administration, environment, and education/research dimensions. This study supports the necessity for a sustainability assessment tool for HEIs.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".