Sustainability assessment in higher education institutions: the stars system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainable development is a concern for countries, businesses and organizations sensitive to excess in terms of utilized resources. This is evident in international initiatives which aim to establish guiding principles for institutions to follow regarding what is considered to be socially responsible behavior, allowing for assessment and the identification of objectives. As higher education institutions, colleges and universities have a public responsibility to generate and transmit knowledge to society as a whole, as well as an economic and social responsibility regarding resource management; hence the importance of specifically analyzing their socially responsible behavior. This paper introduces an initiative which has been implemented in the United States and Canada; one of its aims is to identify best practices in this field and obtain knowledge that allows for the creation and development of a guide to social responsibility adapted specifically to higher education institutions. The Sustainability Tracking, Assessment & Rating System (STARS) is an innovative initiative developed by the Association for the Advancement of Sustainability in Higher Education (AASHE), in which all higher education institutions in the United States and Canada are welcome to participate. Their analysis will allow us to determine which measurable aspects will become a part of the sustainability culture that is developing in the higher education institutions that participate in this initiative. Furthermore, it will allow us to highlight the ethical values that are being promoted among its special interest groups.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it