Notice bibliographique
Résumé
The literature on gentrification has focused predominantly on housing dynamics. To the extent \nthat it has addressed the commercial dimension of gentrification, the emphasis has generally \nbeen on the characteristics of consumers as gentrifiers. With a few exceptions, what is absent \nfrom the literature on commercial change is an analysis of the types, motivations, and experiences \nof commercial establishments in gentrifying neighbourhoods—especially those at risk of \ndisplacement—or strategies for retaining those businesses serving the needs of low-income \nand ethnically mixed residents. A fundamental premise of this research is that retaining such \nbusinesses is crucial to preserving the affordability of neighbourhoods and creating urban \nspaces where people can encounter one another and recognize common interests across social \ndifferences. \nThis study of commercial change in Toronto’s downtown West-Central neighbourhoods explores \nhow commercial change contributes to wider processes of exclusion and gentrification, \nas well as the strategies and resources available to counter this pervasive trend. Specifically, \nthe study has the following objectives: \n to document patterns of commercial change in West Downtown, concentrating specifically \non the characteristics of three commercial strips in different “stages” of commercial gentrification; \n to identify challenges and opportunities that businesses face, particularly those serving lowincome \nresidents, for the purpose of identifying key themes in commercial gentrification; \n to recommend ways to support long-time businesses in the study area through policy \nchange and community organizing. \n“Commercial gentrification” refers to the processes by which long-established businesses providing \nproducts and services affordable to low-income people are leaving downtown Toronto \nneighbourhoods and being replaced by establishments catering to more affluent consumers. \nWhile we adopted the politically neutral language of “commercial change” in our interview questions, \nwe use the term “gentrification” explicitly in this report to underscore our analytical emphasis \non the exclusions, struggles, and displacements associated with the production of commercial \nspace for progressively more affluent users. \nWe selected three commercial strips to represent different characteristics and stages of commercial \ngentrification, based on a combination of anecdotal evidence and data on rates of land \nvalue change. We conducted semi-structured interviews with 10 business owners or managers \non each strip and a representative of the local Business Improvement Area. The businesses \nwere selected to represent both a range of ownership structures and a combination of businesses \nthat serve low-income residents and those that reflect the changing character of gentrifying \nneighbourhoods. \nFinally, we assessed patterns of commercial change over time in the context of the commercial \nstructure on the three strips by examining the “activity code” assigned by the City of Toronto to \neach 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, \nand compared this information with qualitative interview data on the changes taking \nplace. \nOur findings and analysis are presented in two subsections. The first presents key characteristics \nof the commercial strips that we have identified as “rapidly gentrifying,” “gentrifying,” and \n“not-gentrified.” What is clear from these descriptions is that gentrification is not a straightforward \nprocess in which the three commercial strips represent fixed positions along a stable and \npredictable trajectory. Understanding gentrification requires an appreciation of local social histories, \nand how those social histories articulate wider-scale capital flows and shape the opportunities \nand constraints faced by businesses in any given location. \nOur classifications are useful in comparing types and states of neighbourhood change, but the \ngoal is not to generalize about commercial strips. Rather we want to identify themes for discussion \nthat might inform a critical understanding of the complexity of commercial gentrification \nprocesses and potential areas of policy intervention and advocacy to support long-time local \nbusinesses serving the needs of low-income and ethnoculturally diverse residents. \nThe second section is devoted to exploring those themes. It takes up the issues of ownership \nstructure in relation to local investment and perceptions of community; transnationality in relation \nto the commodification of ethnocultural difference, the politics of strip “branding,” and the \nrole of immigrant-owned businesses in building social cohesion; the role of BIAs in both promoting \nlocal development and fragmenting the urban landscape; networks of local retailers, \nconsumers and labour that form clusters of agglomeration; the multiple forms and actors in the \ncommunity economy; and the challenges and opportunities for business finance. \nThe report concludes with some recommendations for policy and community organizing in the \nareas of providing education about the social costs of commercial gentrification, developing \nstrategies to retain businesses that provide affordable goods and services, supporting BIAs in \nlocal asset building and inclusionary practices, and countering fragmentation through comprehensive \nplanning 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 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 tête enseignante, 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 ».