Notice bibliographique
Résumé
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».