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Record W4241624701 · doi:10.25336/p6f61g

Geographies of Ageing: Social Processes and the Spatial Unevenness of Population Ageing

2014· article· en· W4241624701 on OpenAlexaffvenueabout
Herbert C. Northcott

Bibliographic record

VenueCanadian Studies in Population · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAgeingPopulation ageingEconomic geographyPopulationGeographySociologyDemography

Abstract

fetched live from OpenAlex

Aged persons make up an increasing percentage of populations around the world.The major drivers of this trend are declines in fertility and mortality.With fewer children being born and more people surviving into old age, the percentage of seniors increases in the population.The authors note that this population aging trend is ongoing and will continue well into the 21st century, creating an unprecedented shift in age structures worldwide.Nevertheless, the authors note that population aging is variable across various geographical units.That is, there is a spatial variability in population aging.This book explores the reasons for the spatial "unevenness" of population aging, and the implications of these patterns.The authors situate their book as a contribution to the sub-discipline of population geography, which is concerned with the spatial aspects of demographic trends.The book is organized into eleven chapters, grouped into three parts that examine the spatial nature, the determinants, and the implications of population aging.The authors begin by examining population aging as a global phenomenon with spatial variation across regions and countries.At this geographic level, population aging is primarily a function of fertility and mortality rates.The authors then examine spatial aging within countries by focusing on the case of Australia.The authors note that the distribution of the aging population within a country tends to be influenced by migration patterns.For example, out-migration of young adults from rural agricultural areas tends to increase the concentration of seniors who are more likely to stay in these rural areas.On the other hand, in-migration of young adults to cities tends to decrease the concentration of seniors aging in place in the cities.Nevertheless, the authors point out that there are spatial variations in aging within cities. Seniors, for example, tend to be more concentrated in older neighborhoods, as they age in homes they purchased many years ago, while younger families tend to concentrate in the newer suburbs.Further, the authors examine elder migration.The in-migration of seniors to certain lifestyle amenity regions, such as coastal areas near larger cities, illustrates the role that elder migration plays as a factor in the spatial variability of population aging.The authors also note that the international migration of seniors can be very consequential for certain popular retirement amenity destinations.For example, northern European seniors may choose to retire permanently or seasonally in various southern European countries.Both climate and economics are a factor, with seniors attracted to warmer climates and lower costs of living.A similar phenomenon is evident in North America, where Canadian seniors and seniors from the northern states in the USA often relocate to the warmer southern states, either permanently or seasonally.Seniors who relocate are often already disengaged from the labour force, and therefore do not move for employment reasons.In contrast, the authors observe that the movement of younger people tends to be driven by education and employment opportunities.As noted previously, the movement of younger people is a major factor in the uneven distribution of the aging population within a country.Employment opportunities may be

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.380
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.299
Teacher spread0.275 · 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 teacher head, 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

Citations4
Published2014
Admission routes3
Has abstractyes

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