FOOD INSECURITY AT THE UNIVERSITY OF SASKATCHEWAN DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Food insecurity has become a growing issue for many Canadian subpopulations. When the COVID-19 pandemic began in March of 2020, many food-insecure households and individuals were unable to use previously relied-upon services or practises to ease their struggles with food insecurity. This case study focuses on post-secondary students as one disproportionately affected subgroup and uses a critical ethnographic approach paired with an autoethnographic lens to explore the experiences of food insecurity within the population during the pandemic. Drawing on one-on-one interviews and interrogation of her own lived experiences, the researcher draws out the complex, comingled, and often painful realities of food insecurity in the lives of university students. Participants’ struggles to obtain sustenance and their compulsion to minimize the difficulties they face was explored through discussion of matters that particularly affect post-secondary students at the University of Saskatchewan. Combining these intimate interviews with the first-person accounts and reflections of the researcher, who also struggled with food insecurity, resulted in a multiplicative enrichment to the analysis and depth of understanding. The interviewees openly shared their views and perspectives on their distressing experiences struggling with food access during a difficult period in history and painted a somewhat dismal picture of the challenges faced by students. However, despite the critiques they offer of structural barriers, neglect, and inadequate supports, the participants and the researcher remain hopeful, and proffer ideas on how to make changes that could improve the food security of future cohorts of post-secondary students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".