Remaking the Nation-State: Multiculturalism, Neoliberalism, and Urban Revitalization
Bibliographic record
Abstract
My dissertation, Remaking the Nation-State: Multiculturalism, Neoliberalism, and Urban Revitalization, investigates the revitalization of two low-income housing projects in Toronto, Canada: Regent Park and Lawrence Heights. I situate my investigation at the intersection of nation-state/nationalism studies and urban studies and argue that processes of urban revitalization are an important site for the production of national identity and state practices. I examine links between revitalization projects and the construction of the Canadian nation-state by tracing how discourses of multiculturalism and neoliberalism gain currency in urban revitalization projects. \n \nIn particular, I investigate the links between historical urban processes of development and revitalization and North American projects of nation-state formation. I explore this entanglement by tracing what I identify as three distinct technologies that shape and are embedded in the revitalization planning process: discourses of diversity, surveillance, and consultations. I argue that the emphasis on participation of both culturally diverse and entrepreneurial subjects in community consultations and community policing integrates residents into rituals of democracy that are enmeshed with national ideals. My investigation maps this set of social processes to show how they ultimately reproduce exclusion and disparity by regulating diversity, normalizing community policing, and mandating consultations. Through my ethnographic research, I also trace how residents negotiate these processes and make meaning of participation that creates space for their own understandings of surveillance and consultation. My exploration locates the Canadian context in relation to broader examinations of nation-state making and as such can help us to understand the management of sociocultural difference and the neoliberal production of inequality in the contemporary moment.
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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 | 0.003 | 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 | 0.013 | 0.034 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".