Traditional Stereotypes and New Suburbanisms: A Comparative Study of the Vaughan Metropolitan Centre and East Woodbridge in Vaughan, Ontario
Bibliographic record
Abstract
Urbanization is continuing around the world at a rapid rate, necessitating massive investment in developing the core infrastructure of emerging urban societies and overhauling outdated or overloaded systems (Addie, 2016). Residents of traditional suburban neighborhoods continue to experience firsthand the frustrations created by poorly designed suburban infrastructure and transit solutions. To begin, this paper will examine the effects of traditional planning stereotypes on defining the new idea of suburban design and infrastructure. I will examine components such as suburban infrastructure, emerging urban areas, and the role of effective planning as determinants in defining a new suburban life. I will examine how suburban stereotypes, a term first coined by Richard Harris (2018), influence the infrastructure and ultimately the possible transit and movement solutions put in place for an area. In terms of emerging urban areas, Wu and Keil (2022) state that suburban stereotypes also influence the dynamics of how an urban periphery is defined. The authors present a movement away from traditional definitions of urban peripheries and towards diverse peripheries that produce diversified metropolitan landscapes (Wu and Keil, 2022). The role of effective planning is also crucial in diversifying the definition of suburban areas. According to Marcuse (2011), modern planning should involve the use of three different approaches: a technical one, a social reform one, and a social justice one. Planners must therefore be able to move beyond traditional viewpoints to create and accommodate diversification in suburban areas. The goal of this paper is to examine, through the case study analysis of two neighbourhoods in the municipality of Vaughan, whether suburban infrastructure has evolved enough to meet the needs of emerging urban peripheries in comparison to traditional suburban stereotypes. As Addie (2019) has stated, suburban infrastructure is defined as a means of ordering suburban form and providing conditions essential to the life and functioning of a society. There is importance in researching this topic because the implementation of suburban infrastructure in creating new urban peripheries must include discussions involving all supporting players. To Addie (2016), this includes the conceptualization of a city and the nature of social justice within it, not just the infrastructure itself. Investment in infrastructure for the long-term would drive expansion in other interconnected urban factors, such as construction, energy, transportation, and water and waste (Keil, 2013). The paper reveals that suburban growth is not solely about the homes that are built, but also the socio-economic factors that support them, such as transit access, walkability, and work and social opportunities. As such, literature suggests that new urban design must be transitoriented and part of suburban land use planning (Dock and Swenson, 2003). Defining suburban transit, housing, and social infrastructure solutions, and incorporating them into master plans before a community is built or expanded is becoming increasingly important. The paper concludes with a set of ideas on how to minimize the negative physical, mental, and emotional consequences of traditional suburban stereotypes, and provide incentive to implement the new determinants of suburban life instead.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".