Notice bibliographique
Résumé
This dissertation examines the claim that Age Friendly Cities (AFCs) represents an effective and revolutionary policy approach to population aging. The AFC approach is a placebased policy program intended to enhance the ‘fit’ between senior citizens and their environment. Mainstream accounts of AFCs claim that the program represents a paradigmatic shift in the way we think about aging, to move away from an individual health deficit approach to one that seeks to improve local environments by empowering seniors and local policy actors. However, initial critical literature notes that while AFCs may offer the potential to expand social and physical infrastructure investments to accommodate diverse population needs, they are being popularized in a conjuncture where the public sector is being restructured through narrow projects of neoliberalism that call for limiting public redistribution. This literature calls for further empirical studies to better understand the gap between AFC claims and practice. I heed this call through a qualitative case study of AFCs in the City of Toronto; a particularly relevant case because the recent Toronto Seniors Strategy has been critiqued for being more symbolic than substantive. My research represents a critical policy study as I understand AFCs not as a technical policy tool but as a political object attractive to conflicting progressive and neoliberal projects that use rhetorical and practical strategies to ensure their actualization. My approach is normative as I seek to provide insight for a transformative ‘right to the city’ for senior citizens through the AFC approach. I use literature on citizenship to understand the multiplicity of political projects that seek to expand or narrow the relations between people, environments and institutions through the AFC program. This understanding is based on the meanings 82 different policy actors from local government, the non-profit sector, academia, and other levels of government make of their everyday work in creating age-friendly environments. The broad question I ask is: How do local policy actors understand the rhetoric and practice of AFCs in Toronto and how do these understandings illustrate particular expansive and narrow political projects that affect the development of a right to the city for senior citizens through this policy program? I begin with an initial Case Chapter that scopes age friendly policy work in Toronto from a ‘seeing like a city’ perspective that identifies the complex multi-scalar and multi-actor nature of this policy domain. The Recognizing Seniors and Role of Place Chapters then examine AFCs rhetorically with respect to how local policy actors understand the ‘person’ and the ‘environment’. The Rescaling Redistribution and Restructuring Governance Chapters explore the practice of AFCs, including how local policy actors understand their capacities to design and deliver age-friendly services and amenities and the institutional mechanisms at their disposal to action AFCs. My findings challenge the claim that the AFC policy approach is effective, let alone revolutionary. I learn from policy actors that narrow projects of restructuring work to assemble seemingly progressive rhetoric and practice around active aging and localism to reduce universal public provision, expand the role of private citizens and their families to provide care, and use local policy actors as residual providers of last resort. My research documents how more expansive understandings of senior citizens as rights bearers and the role of the public and non-profit sector to recognize and redistribute on this basis are also in operation. Understanding these political projects more deeply through the AFC policy program helps me to offer policy insight as to what is needed both rhetorically and practically to craft a more effective and revolutionary alternative AFC model based on a right to the city for senior citizens.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,040 | 0,027 |
| Communication savante | 0,009 | 0,004 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».