Universities and Sustainable Food Practices: An International Comparison Along the Pacific Coast of North America
Bibliographic record
Abstract
My research project examined sustainable practices in relation to food sources at three universities located along the Pacific Coast of North America. The academic institutions were the University of Alaska Anchorage (UAA), the University of California Santa Barbara (UCSB), and the University of British Columbia (UBC) in Vancouver. These schools were selected because while they each foster an abundance of “local” foods and food industries, they represent different stages in the three pillars of sustainability for food practices. My project sought to understand the role of each institution in building a local and sustainable food culture at each university. I analyzed how the food services at and near the institutions reflect the food and nutritional needs and wants of the student body, faculty, and staff of academic institutions and the availability of foods (e.g., healthy, organic, and sustainable) in each area. Using qualitative data acquired through interviews and source-based literature, I classified the three universities in relation to the three pillars of sustainability, namely economic, social, and environmental. UAA was at an early stage of sustainability achievement while UBC was the most developed. In comparison, I evaluated Western Kentucky University (WKU) as situated between UAA and UCSB. Key steps to successful sustainability of food resources include creating local and regional food resources, engaging students, faculty and administrators, and developing an economically feasible institutional vision. Institutions of higher learning have a strong influence on their region and with forethought and planning, they can serve as drivers of sustainable food systems.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".