No. 36: ‘Going to the Supermarket was Hard’: Pandemic Foodscapes and Unsettled Food Practices of Refugees in the Waterloo Region
Bibliographic record
Abstract
In this paper, we examine how the ‘new normal’ of pandemic-living transformed the local food environment in Ontario as pandemic foodscapes. Using selected findings from mixed methods research with a small sample of recently resettled refugees in the Waterloo region, we evaluated how these changes affected their grocery shopping and food-sourcing habits. We identify the distinctive ways the pandemic-related restrictions altered our participants’ interactions with their local food environment and influenced their food availability and accessibility. Our study found that participants spent more time acquiring food from a reduced number of food sources and experienced an overall weakening of their household food security. The decline in food access and availability was most pronounced for the ethnocultural foods that immigrants and refugees preferred to consume. We offer a nuanced understanding of how the broad set of circumstances of our respondents and their household members shaped their mobility experiences about food provisioning. Most participants attempted to minimize their trips to purchase groceries due to the risk of coronavirus but were unable to do so, especially in large households. An insignificant segment of the study cohort successfully followed new adaptation modes, such as grocery delivery, because of associated costs. Moreover, vulnerable sections of our research cohort drastically limited their food provisioning and remained greatly dependent on their social networks’ assistance, generosity, and circumstances in acquiring groceries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".