Territorial Stigmatization and Territorial Destigmatization: A Cultural Sociology of Symbolic Strategy in the Gentrification of Parkdale (Toronto)
Bibliographic record
Abstract
Abstract Territorial stigmatization is one of the most powerful concepts for understanding how social, spatial and symbolic processes are intertwined in producing contemporary urban inequality. Through a detailed case study of Parkdale, a Toronto neighbourhood that has been profoundly shaped by its long association with poverty, single room occupancy housing and psychiatric survivors, this article works at the points of intersection between the rapidly expanding literature on territorial stigmatization and wider social scientific interest in gentrification‐led displacement. Drawing on archival research, participant observation and interviews with residents, it demonstrates how territorial stigmatization, and a new allied concept, territorial destigmatization , operate in Parkdale. Territorial stigmatization and destigmatization work across three dimensions: legal, material and discursive. Using conceptual tools from cultural sociology to foreground symbolic elements of these three dimensions, two strategies of territorial destigmatization are delineated: one that operates in concert with gentrification‐led displacement, and the other that works to symbolically reinscribe stigmatized persons and housing forms. To complement and sharpen territorial stigmatization research, recent findings from studies of stigma are integrated to show how psychiatric survivors and housing advocates in Parkdale use territorial destigmatization to offset gentrification‐led displacement.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.059 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".