From insight to action: Possible pathways for sustainable futures in a Canadian university
Bibliographic record
Abstract
This study examines the impact of the Thriving Futures 2023 event, which engaged the Dalhousie University (DAL) community located in Halifax, Nova Scotia, Canada, and residents from the broader Halifax area in exploring inclusive sustainable development pathways. Employing a mixed methods approach that included surveys, video interviews, and expressive arts, the research captures diverse perspectives from students, staff, faculty, and community members. Rooted in a transdisciplinary framework, the event wove together the 17 Rooms methodology with Indigenous and local knowledge systems. Through deep dialogues and collaborative activities focused on the Sustainable Development Goals (SDGs), the event cultivated meaningful engagement. Key findings reveal a strong enthusiasm and readiness within the academic community to advance sustainability efforts, alongside challenges such as limited structural incentives and insufficient university leadership support. This study underscores the critical role of inter- and transdisciplinary collaboration, inclusive leadership, and the integration of sustainability principles into university curricula and operations. By reflecting on the outcomes of Thriving Futures (2023), the research offers actionable strategies for embedding sustainable practices in higher education and contributes to the broader discourse on applying the SDGs in academic contexts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.055 | 0.020 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".