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Inner Suburbs at Stake: Investing in Social Infrastructure in Scarborough

2011· article· en· W3152714324 sur OpenAlex

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Notice bibliographique

RevueTSpace · 2011
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHousing, Finance, and Neoliberalism
Établissements canadiensnon disponible
Organismes subventionnairesIJURR Foundation
Mots-clésBusinessEnvironmental planningGeography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

It is now impossible to ignore that Toronto is becoming a divided city. Stacks of research
\nconfirm trends that are plainly visible in the urban landscape: social polarization, spatial
\nsegregation, and a deepening racialization of poverty are defining features of our city’s
\nsocial geography. These trajectories come together in powerful ways in the city’s inner
\nsuburbs. Increasingly home to communities of people living in concentrated poverty, the
\nresidents of low‐income inner suburban communities are also increasingly people of
\ncolour.
\nThese trends are not new. As early as 1979, the Social Planning Council of Metropolitan
\nToronto reported these emerging patterns, sounding a call to government, service
\nproviders and researchers. These trends are also not operating in isolation. Despite the
\nwidespread concern for “suburban decline,” population change and disinvestment in the
\ninner suburbs are part of metropolitan scale urban change that is also propelling the
\ndowntown core to become wealthier and whiter. This latter trend is rarely understood as a
\nproblem, even as it too is constitutive of a dividing city.
\nDespite the longstanding nature of these patterns and their metropolitan scale, they have
\ngarnered a flurry of public and policy attention over the past few years that focus
\nspecifically on the inner suburbs. The most prominent initiative – crafted jointly by the City
\nof Toronto and the United Way of Greater Toronto – is the “Priority Neighbourhood” (PN)
\nstrategy. The PN strategy has two central aims: to invest in underserviced communities and
\nto transform the way in which local residents, city staff and service providers participate in
\ncommunity planning. Targeted investment is geared toward building physical and social
\ninfrastructure in underserviced communities, while the emphasis on community‐based
\nplanning and resident engagement aims to cultivate collaboration across non‐profit, public,
\nand resident organizations, spur creative projects and initiatives, and empower residents. The PN framework has important goals and has achieved notable success, yet it is also
\ninfused with perennial problems. Architects of the PN strategy mobilize a controversial
\nliterature on “neighbourhood effects,” which brackets the broader context for
\nneighbourhood change and may place responsibility for poverty on the residents of lowincome
\nneighbourhoods. As critics of this literature have charged, the exclusive focus on
\nthe neighbourhood scale misdiagnoses poverty as a purely local problem rather than as a
\ncomplex problem that manifests itself locally. Solutions to poverty that focus exclusively on
\nthe local scale sideline well‐documented causes of segregation, polarization, and
\nracialization that stem from broader forces such as the economy and government policy.
\nIronically, approaches rooted in the neighbourhood effects literature often avoid
\naddressing poverty directly, focusing instead on cultural or behavioural change such as the
\ncultivation of civic engagement. Finally, spatially targeted policy oversimplifies the
\ncomplexity of social networks and everyday life, creating arbitrary boundaries for
\nresidents and agencies in accessing resources.
\nFor targeted investment to be effective, it needs to be seen as one ingredient in a broader
\nstrategy for change and not an end in itself.
\nIndeed, this report finds that some “neighbourhood strategies” are more effective than
\nothers. Drawing on a pilot study that contrasts the experiences of the Kingston‐
\nGalloway/Orton Park (KGO) priority neighbourhood, with Parkdale, a downtown
\ncommunity that faces similar social and economic challenges but which did not receive PN
\ndesignation, the report demonstrates that how we diagnose the problems in
\nneighbourhoods matters profoundly in how we respond. This research further suggests
\nthat there are different ways of understanding neighbourhoods active within the PN
\nstrategy. According to residents and community workers, some ways of making sense of
\nneighbourhoods and making change in neighbourhoods are more effective and responsive
\nthan others, and this report explores these strategies and practices in some detail. It
\nincludes findings about both effective and ineffective strategies.
\nEffective neighbourhood strategies cultivate social infrastructure. They stem from
\nexplanations for concentrated poverty that assign responsibility to government policy and
\neconomic change at the local, regional, national, and global scale. They restore investment
\nin human services and facilities in areas that have been overlooked, but they also advocate
\nchange at scales much larger than the local in order to respond to social polarization,
\nsegregation, and the racialization of poverty. Effective strategies for neighbourhoods are
\ntailor‐made for local conditions by local communities. They are accountable and inclusive,
\nprovide meaningful skills development that responds directly to identified gaps and needs,
\nand they explicitly address persistent inequalities such as those that are manifest along the
\nlines of race, mental health, class, and gender.
\nIneffective neighbourhood strategies in Toronto tend to drain local capacity. They often
\nassume cultural explanations for concentrated poverty that assign responsibility to lowincome
\nneighbourhoods or the people who live in them. Ineffective neighbourhood strategies download responsibility for problems experienced in neighbourhoods to the
\nneighbourhood itself and yet extend little or no voice and authority to residents of those
\nneighbourhoods. Ineffective strategies take a top‐down, cookie‐cutter approach to
\nneighbourhood investment, undermining the agency and autonomy of the communities
\nthey ostensibly aim to support.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,169
Score d'incertitude au seuil0,857

Scores Codex et Gemma par catégorie

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

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 Opus0,064
Tête enseignante GPT0,255
Écart entre enseignants0,192 · 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