Bibliographic record
Abstract
This thesis explores the socio-cultural and systemic drivers of food waste among university students at the University of Victoria (UVic) in Canada and Jaramogi Oginga Odinga University of Science and Technology (JOOUST) in Kenya. Drawing on Social Practice Theory (SPT) and Political Ecology (PE) theoretical frameworks, the research investigates how cultural norms, institutional policies, and infrastructural factors contribute to food waste in these academic environments. Using a combination of focus groups, photo narratives, and semi-structured interviews with students, faculty, and food service personnel, the findings reveal that food waste is shaped by large portion sizes, dissatisfaction with taste, limited storage options, and social and cultural norms. At UVic, the rigid meal plan system promotes over-serving, while JOOUST’s pay-as-you-eat system, though seemingly fairer, carries the risk of over-purchasing, influenced by communal dining practices. The study emphasizes that addressing food waste in higher education institutions requires a holistic approach that goes beyond individual behaviors to consider the broader cultural and systemic factors at play. By recognizing the influence of both student practices and institutional constraints, this thesis highlights the need for targeted, context-specific interventions to foster sustainable food consumption and waste reduction on university campuses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".