The comprehensiveness of competing higher education sustainability assessments
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the comprehensiveness of competing higher education sustainability assessments. Higher education institutions (HEIs) have been increasingly communicating their sustainability commitments to the public. To assist the public in evaluating these claims, a broad range of actors have assessed the sustainability of HEIs. Design/methodology/approach The paper uses an evaluation framework (the GRI-HE) consisting of criteria developed by the Global Reporting Initiative and the Association of University Leaders for a Sustainable Future to analyze the comprehensiveness of nine publicly-available frameworks that have been used to assess HEI sustainability. Findings While finding that in general these assessments are not comprehensive and particularly lack coverage of the social and economic dimensions of sustainability, the paper identifies the Pacific Sustainability Index and Sustainability Tracking and Assessment Rating System (STARS) as the most comprehensive assessments in the sector. Research limitations/implications This study does not assess the quality of the match to the GRI-HE’s criteria, only whether they match to a reasonable degree. The analysis highlights areas where each HEI sustainability assessment framework can add criteria and improve their comprehensiveness and validity. Future research should explore the causes and relative importance of the gaps in these frameworks. Originality/value The paper provides a valuable discussion and demonstration of the use of comprehensiveness as a proxy metric for the validity of sustainability assessments. This analysis is the first detailed, comprehensive and transparent analysis of HEI sustainability assessments based on a broad-based and widely accepted set of criteria.
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 | 0.137 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| 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".