No. 33: Living Through the COVID-19 Pandemic as a Refugee in Secondary Cities in Canada: The Intersectionality of Immobility, Gender and Food Insecurity
Bibliographic record
Abstract
The COVID-19 pandemic exposed and exacerbated the vulnerabilities of migrants and refugees in secondary cities in Canada, where the restrictive food environment and limited resources heightened challenges related to food security. This study investigates how the intersectionality of immobility, gender, and food insecurity shaped the lived experiences of recently resettled Syrian, Somali and Afghanistan refugees in the Waterloo Region, Canada, during the pandemic. The mixed methods research approach integrates survey and in-depth interview data to examine refugees’ motivations for migration, economic conditions, challenges in accessing culturally appropriate food, and the impact of gender roles. Findings reveal that structural barriers within the food environment, compounded by mobility restrictions and shifting gender dynamics, perpetuated a vicious cycle of marginalization that undermined migrants’ overall well-being. Women respondents were particularly affected as primary caregivers, by bearing the disproportionate burdens of food-related household responsibilities under precarious circumstances. This paper contributes to the discussion on migration, food systems, and social inequalities by emphasizing the need for gender-responsive and culturally sensitive policies to address the compounded challenges refugees encounter during crisis circumstances.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".