An aggregated and dynamic analysis of innovations in campus sustainability
Bibliographic record
Abstract
Purpose This paper aims to compose a systematic understanding of campus sustainability innovations and unpack the complex drivers behind the elaboration of specific innovations. More precisely, the authors ask two fundamental questions: What are the topics and modes of implementation of campus sustainability innovations? What are the external and internal factors that drive the development of specific innovations? Design/methodology/approach The authors code and analyze 454 innovations reported within the Sustainability Tracking Assessment and Rating System (STARS), the campus sustainability assessment tool of the Association for the Advancement of Sustainability in Higher Education. Using descriptive statistics and illustrations, the paper assesses the state of environmental innovations (EIs) within STARS. Then, to evaluate the role of internal and external drivers in shaping EIs, the authors have produced classification and regression tree models. Findings The authors’ analysis shows that external and internal factors provide incentives and a favorable context for the implementation of given EIs. External drivers such as climatic zones, local income and poverty rate drive the development of several EIs. Internal drivers beyond the role of the agent of change, often primarily emphasized by past literature, significantly impact the implementation of given EIs. The authors’ work also reveals that EIs often move beyond traditional mitigation approaches and the boundaries of campus. EIs create new dynamics of innovation that echo and reinforce the culture of a higher education institution. Originality/value This work provides the first aggregated picture of EIs in the USA and Canada. It produces a new and integrated understanding of the dynamics of campus sustainability that complexifies narratives and contextualizes the role of change agents.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".