The Biopolitics of Gentrification:Re-imagining and Re-membering London, Ontario's Old East Village
Bibliographic record
Abstract
In this thesis I present a critical discourse analysis that examines the ways in which people, places, and populations are managed through the gentrification of London Ontario’s Old East Village (OEV). In particular, I am concerned with the manner in which gentrification of this poor, post-industrial neighbourhood mobilizes discourses that stigmatize disenfranchised people and discard populations in order to “revitalize” disinvested places and re-form cities in ways that valorize a particular kind of personhood, one that contributes to economic and social recovery through neoliberal notions of entrepreneurialism and communitarianism. My concerns are informed by theoretical notions of stigma power, biopower, and biopolitical racism, which I use to frame gentrification as a biopolitical undertaking that aims to devalue, dehumanize, and displace stigmatized groups who are constructed as threatening the area’s economic vitality and the well-being of the general population. Drawing on multiple sites of discourse production (municipal policy and planning documents, local news media, interviews, streetscapes), my analysis attends to the ways in which this area and the people who inhabit and occupy this space are reimagined and re-membered through discourses of nostalgia, sanitization, and community, which excavate and redefine the city’s core, securitize the neighbourhood from constructed threats, and reconstruct belongingness as defined by civic responsibility and embodied by entrepreneurs and communitarians. I also explore how contestations about revitalization are articulated within these discursive re-imaginings and re-memberings. My findings illuminate the ways in which the discursive rebirth of London’s OEV cleaves from belonging old life, Indigenous life, and poor life: senior lives are rendered obsolescent through prospective nostalgia; Indigenous lives are erased through a selective remembering of the area’s white settler heritage; and poor lives (those who are unhoused, substance-addicted, and/or mentally ill) are expelled through discourses of de/stigmatization and sanitization that amplify the threat they pose to the reimagined space. Within this process of revitalization, reimagining is a practice of violence enacted through biopolitical measures to install a preferred population for whom civic and economic contribution are prerequisites for community belongingness.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.073 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".