Seven recommendations for creating sustainability education at the university level
Bibliographic record
Abstract
Purpose This paper describes a set of recommendations that will aid universities planning to create sustainability education programs. These recommendations are not specific to curriculum or programs but are instead recommendations for academic institutions considering a shift towards “sustainability education” in the broadest sense. The purpose of this research was to consider the possible directions for the future of sustainability education at the university level. Design/methodology/approach Through a series of workshops using a “value focused thinking” framework, a small team of researchers engaged a large number of stakeholders in a dialogue about sustainability education at the University of British Columbia (UBC), Vancouver, Canada. Recommendations were compiled from workshop data as well as data from 30 interviews of participants connected with decision‐making and sustainability at UBC. Findings The recommendations include infusing sustainability into all university decisions, promoting and practicing collaboration and transdisciplinarity and focusing on personal and social sustainability. Other recommendations included an integration of university plans, decision‐making structures and evaluative measures and the integration of the research, service and teaching components of the university. There is a need for members of the university community to create space for reflection and pedagogical transformation. Originality/value The intention of the paper is to outline the details of a participatory workshop that uses value‐focused thinking in order to engage university faculty and administration in a dialogue about sustainability education. Students, faculty and staff working towards sustainability education will be able to adapt the workshop to their own institutions.
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 | 0.064 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".