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Record W7055722878

Dimensions of age and aging in Toronto: An inter-decade socio-ecological analysis

2022· dissertation· en· W7055722878 on OpenAlexafffundabout

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsRegional Municipality of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeighbourhood (mathematics)Population ageingLife course approachPopulationBaby boomersPublic policyAging in place
DOInot available

Abstract

fetched live from OpenAlex

The aging of global populations long forecasted by demographers, governments, and other public and private actors is now rapidly being realized in many countries around the world, particularly in advanced, industrial economies like Canada. Driving this population aging are members of the Baby Boomer generation, a group larger and in many ways more socially influential than preceding birth cohorts, that are now entering life’s later stages and (if social theorists are correct) redefining concepts of older adulthood we currently rely on to plan for the aged. However, the portended impacts of this aging and proposed policy responses largely remain focused at the national/provincial level, with scant attention paid to how the aging of community and neighbourhood populations will occur and how aging will impact these local spaces. Only in recent years have researchers seriously attempted to understand how age and aging overlap the other complex forces that structure urban space and influence how neighbourhoods change. Drawing on theories of social ecology, this thesis assesses the roles age and aging play in urban structure and changes processes, using perspectives of life stage and generation to discern how the aging of Baby Boomers is enmeshed therein. Using the City of Toronto, Canada as study area, this research employs factorial analysis – here, principal component analysis – on a sample of 468 Toronto neighbourhoods for which a comprehensive dataset of social and spatial measures, with an emphasis on age, is created for the years 1996, 2006, and 2016 and for the decades 1996 to 2006 and 2006 to 2016. A set of components are generated for each year and period to serve as measures of dimensions which underlie how these measures relate e.g., how age and aging relate to Toronto’s other social and spatial elements; importantly, these components are also mapped to reveal of how different parts of Toronto reflect the conceptual constructs depicted. These sets of components and their spatial patterning are then assessed for their analytical import, focusing on where and how age and aging overlap other elements of Toronto’s urban social ecology. Findings reveal that while Toronto continues to be primarily organized by socioeconomics that are heavily inflected by ethnic and immigrant status, age still plays a vital role structuring the city’s social ecology, a role more complex than foundational theories account for, even if these are useful for understanding how the aging of residents interacts with neighbourhoods’ other social and spatial elements. Further and in terms of how Toronto changes, while other social elements appear to crystalize, or remain stable between years, resident aging takes a more prominent role defining the changes Toronto’s neighbourhoods are undergoing. As for where Baby Boomers factor into this, while the earliest-born half of the generation follows a similar trajectory as preceding generations in entering older age, the younger half of the generation diverges from this trajectory and thus from established norms of life’s later stages. Moving forward, it seems age and aging are becoming a more definitive a factor in the structuring of urban environments like Toronto and that concepts of life stage and generation, that have been developed more concretely in other research disciplines, will be crucial for continuing to unravel the complex ways in which demography interweaves itself into urban social ecology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes3
Has abstractyes

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