Living through a Pandemic in the Shadows of Gentrification and Displacement: Experiences of Marginalized Residents in Waterloo Region, Canada
Bibliographic record
Abstract
Introduction What happens to marginalized communities that were already facing gentrification and displacement pressures when a major pandemic arrives? This chapter engages with, listens to and amplifies the experiences of very low-income and unsheltered residents as they deal with the pre-existing conditions of extreme housing challenges and the arrival of the first wave of COVID-19. This chapter is part of a wider collaboration between the researchers at University of Waterloo (UW) and the Social Development Centre Waterloo Region (SDC), a charitable non-profit, social planning, and community development organization that focuses on advancing social justice and documenting the lived experiences of poverty and homelessness. Throughout the late spring and summer of 2020, we interviewed residents living through both gentrification and the pandemic. In this chapter, we focus on the everyday lives, challenges, experiences, and opportunities of some of the most marginalized members of our community. The pandemic brought new challenges into a landscape that was already hostile to low-income people. Our chapter seeks to amplify their voices and experiences, which is essential for achieving equitable policy outcomes. At the same time, we juxtapose their experiences with some of the dominant narratives of how COVID-19 has impacted the region. The gentrification context Our case study is the Region of Waterloo, which is comprised of three contiguous mid-sized cities (Kitchener, Waterloo, and Cambridge) and four rural townships. It ranks among Canada's fastest growing urban areas and has a total population of approximately 620,000. The region is situated 100km west of Toronto, Canada's largest city. Kitchener is the largest of the three cities; like many mid-sized communities, its downtown underwent several decades of decline, beginning in the 1980s. Large old homes were divided up into rooming houses, a cluster of social services organizations emerged, and downtown Kitchener became home to much of the region's very low-income population. However, over the past decade, a combination of publicand private-investment in the downtown core has led to a remarkable transformation and regeneration, unlike any other mid-sized city in Ontario. Much of this was spurred by the development of a new Light Rail Transit Line, which opened in 2019 and was financed by the Region of Waterloo. Even before a single passenger was carried, more than $3 billion worth of investment was made along the 19km route, much of it in downtown Kitchener.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".