Toponymic assemblages, resistance, and the politics of planning in Vancouver, Canada
Bibliographic record
Abstract
The marginalized and impoverished Downtown Eastside neighbourhood of Vancouver, Canada has long been subjected to planning programs that have aimed to solve area problems through strategic government intervention. The 2011–2014 Local Area Planning Process, led by the City of Vancouver in consultation with local actors, represents the most recent of such programs. Despite the Local Area Planning Process’s stated goal of inclusive participation, the resultant Downtown Eastside plan transformed the political landscape of the neighbourhood and met with derision from stakeholders for its potential to generate dramatic capital-led transformations. In this paper, we critique participatory planning through a case study of the Local Area Planning Process. We utilize a lens of critical toponymy (the investigation of the historical and political implications of place naming) as a methodological tool to examine planning technologies of power and their mobilization through governmental processes. We deploy a novel approach to toponymy, drawing on assemblage theory, that presents toponymy as a radically open and dynamic process mobilized relationally through a multiplicity of discourses and materialities. Our case study demonstrates that processes of toponymic assemblage within the Downtown Eastside Local Area Planning Process worked to (1) generate new territorial conflicts, (2) depoliticize community activism, and (3) co-opt racialized and class-based histories of displacement and dispossession to stimulate “revitalization” (“Japantown”). On the other hand, we found that in unanticipated ways, these processes worked to stimulate anti-gentrification activism, alliances, and resistance. Our analysis of planning highlights how toponymic agency can service oppressive and marginalizing place-framings, but it can also have liberating effects – by inspiring unlikely alliances and counter-framings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.051 | 0.023 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".