MétaCan
Menu
Retour à la cohorte
Enregistrement W3147516159

Commercial Change in Toronto’s West-Central Neighbourhoods

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

Notice bibliographique

RevueTSpace (University of Toronto) · 2008
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHousing, Finance, and Neoliberalism
Établissements canadiensnon disponible
Organismes subventionnairesSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
Mots-clésGeography
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,204

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0060,002
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,230
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations13
Publié2008
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueTSpace (University of Toronto)Même sujetHousing, Finance, and NeoliberalismTravaux en français237 207