Developing a quantitative tool for sustainability assessment of HEIs
Bibliographic record
Abstract
Purpose Implementation of a sustainability paradigm demands new choices and innovative ways of thinking. The main objective of this paper is to provide a meaningful sustainability assessment tool for make informed decisions, which is applied to higher education institutions (HEIs). Design/methodology/approach The objective is achieved by developing a quantitative tool for sustainability assessment using a driving force‐pressure‐state‐exposure‐effect‐action (DPSEEA) framework. The DPSEEA framework considers environmental, social, economic, and educational performance as main dimensions of sustainability. The proposed model is called DPSEEA‐Sustainability index Model (D‐SiM). The D‐SiM is a causality‐based model in which the sustainability index (SI) is an outcome of nonlinear effects of sustainability indicators in various stages of DPSEEA. To have an improved understanding of input factors (driving forces) and their impact on sustainability, a simplified empirical model is developed and applied to HEIs to determine the percent contribution of various driving forces on sustainability. Findings The study reveals that economic development, social equity, and education in sustainability are the major drivers for achieving sustainability in HEI, while health and safety issues, energy requirements, institutional enhancement, and international research and development trends are the less significant driving forces. Originality/value The indicators connected in DPSEEA framework through causal relationships lead to the quantitative assessment of sustainability, which provides a unique approach for informed decision making.
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 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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".