Responsible consumption and production for wholistic transformation of universities: Campus innovation through a sustainable livelihoods approach
Bibliographic record
Abstract
Of the 17 UN Sustainable Development Goals (SDGs) adopted as global goals from 2015 to 2030, Sustainable Development Goal #12 on Responsible Consumption and Production has been described by some as the “heart” of the Goals: our production systems are central to meeting our human needs and aspirations along with important dimensions of human well-being (SDGs #1-5); they shape and organize our economic practices, our choice of material inputs, and their efficient and effective long-term sustainable use (SDGs #6-11); they also ensure whether or not we live within the carrying capacity of our natural ecosystems and whether these systems are healthy and resilient (SDGs #13-15). Yet in addition to these social, economic, and environmental sustainability goals, SDG 12 is central at a deeper cultural level. It enables (and is enabled by) peace, justice, and strong institutions (SDG 16), and motivates new productive partnerships between a variety of organizations at all geographic scales (SDG 17 on “Partnerships for the Goals”). This paper explores the cultural dimensions of SDG 12 through its potential for a whole institution approach on university campuses. In particular, its role in reshaping university governance and scholarly identities (particularly around the concept of sustainable livelihoods and political dimensions as discussed by Ian Scoones (2015), understandings of scholarly impact, and greater inclusivity of our disciplinary specializations (through living laboratories and development of transformative technologies) are explored. The University of Regina in Saskatchewan, Canada, is then used as a case study to illustrate these cultural dimensions in the recent development of its strategic plan and living laboratories on campus. The paper demonstrates that a traditional sustainable livelihoods approach to development can be used to create a new sustainable scholarly livelihood identity on campuses. This new scholarly identity allows for substantive transformations in university governance to advance campus sustainability, address core livelihood anxieties within communities, and develop key innovations in the scholarship of sustainable livelihoods.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".