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Record W3147516159

Commercial Change in Toronto’s West-Central Neighbourhoods

2008· article· en· W3147516159 on OpenAlexfundaboutno aff
Katharine N. Rankin

Bibliographic record

VenueTSpace (University of Toronto) · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsGeography
DOInot available

Abstract

fetched live from OpenAlex

The literature on gentrification has focused predominantly on housing dynamics. To the extent that it has addressed the commercial dimension of gentrification, the emphasis has generally been on the characteristics of consumers as gentrifiers. With a few exceptions, what is absent from the literature on commercial change is an analysis of the types, motivations, and experiences of commercial establishments in gentrifying neighbourhoods—especially those at risk of displacement—or strategies for retaining those businesses serving the needs of low-income and ethnically mixed residents. A fundamental premise of this research is that retaining such businesses is crucial to preserving the affordability of neighbourhoods and creating urban spaces where people can encounter one another and recognize common interests across social differences. This study of commercial change in Toronto’s downtown West-Central neighbourhoods explores how commercial change contributes to wider processes of exclusion and gentrification, as well as the strategies and resources available to counter this pervasive trend. Specifically, the study has the following objectives: 􀂃 to document patterns of commercial change in West Downtown, concentrating specifically on the characteristics of three commercial strips in different “stages” of commercial gentrification; 􀂃 to identify challenges and opportunities that businesses face, particularly those serving lowincome residents, for the purpose of identifying key themes in commercial gentrification; 􀂃 to recommend ways to support long-time businesses in the study area through policy change and community organizing. “Commercial gentrification” refers to the processes by which long-established businesses providing products and services affordable to low-income people are leaving downtown Toronto neighbourhoods and being replaced by establishments catering to more affluent consumers. While we adopted the politically neutral language of “commercial change” in our interview questions, we use the term “gentrification” explicitly in this report to underscore our analytical emphasis on the exclusions, struggles, and displacements associated with the production of commercial space for progressively more affluent users. We selected three commercial strips to represent different characteristics and stages of commercial gentrification, based on a combination of anecdotal evidence and data on rates of land value change. We conducted semi-structured interviews with 10 business owners or managers on each strip and a representative of the local Business Improvement Area. The businesses were selected to represent both a range of ownership structures and a combination of businesses that serve low-income residents and those that reflect the changing character of gentrifying neighbourhoods. Finally, we assessed patterns of commercial change over time in the context of the commercial structure on the three strips by examining the “activity code” assigned by the City of Toronto to each individual business on the strips. We conducted statistical tests to identify whether changes in activity codes on the strips between the years 2000 and 2005 were statistically significant, and compared this information with qualitative interview data on the changes taking place. Our findings and analysis are presented in two subsections. The first presents key characteristics of the commercial strips that we have identified as “rapidly gentrifying,” “gentrifying,” and “not-gentrified.” What is clear from these descriptions is that gentrification is not a straightforward process in which the three commercial strips represent fixed positions along a stable and predictable trajectory. Understanding gentrification requires an appreciation of local social histories, and how those social histories articulate wider-scale capital flows and shape the opportunities and constraints faced by businesses in any given location. Our classifications are useful in comparing types and states of neighbourhood change, but the goal is not to generalize about commercial strips. Rather we want to identify themes for discussion that might inform a critical understanding of the complexity of commercial gentrification processes and potential areas of policy intervention and advocacy to support long-time local businesses serving the needs of low-income and ethnoculturally diverse residents. The second section is devoted to exploring those themes. It takes up the issues of ownership structure in relation to local investment and perceptions of community; transnationality in relation to the commodification of ethnocultural difference, the politics of strip “branding,” and the role of immigrant-owned businesses in building social cohesion; the role of BIAs in both promoting local development and fragmenting the urban landscape; networks of local retailers, consumers and labour that form clusters of agglomeration; the multiple forms and actors in the community economy; and the challenges and opportunities for business finance. The report concludes with some recommendations for policy and community organizing in the areas of providing education about the social costs of commercial gentrification, developing strategies to retain businesses that provide affordable goods and services, supporting BIAs in local asset building and inclusionary practices, and countering fragmentation through comprehensive planning measures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.040
GPT teacher head0.230
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2008
Admission routes2
Has abstractyes

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