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Record W2136230071 · doi:10.25336/p64p51

The Conundrum of Demographic Aging and Policy Challenges: A Comparative Case Study of Canada, Japan and Korea

2009· article· en· W2136230071 on OpenAlexvenueaboutno aff
Susan A. McDaniel

Bibliographic record

VenueCanadian Studies in Population · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPopulation ageingFraming (construction)PopulationEconomic shortageDemographic economicsWork (physics)Development economicsProductivityPolitical scienceEconomicsEconomic growthSociologyGeographyDemographyGovernment (linguistics)

Abstract

fetched live from OpenAlex

Some analysts lean toward comparative analyses of population aging, then draw potential policy implications. Others lean in the direction of attention to differences in policy regimes and then consider implications of population aging. Key differences among advanced societies may not emanate from demographic aging but from differences in how markets, states, and families work to redistribute societal benefits. In this paper, three countries with contrasting configurations of markets, states, and families, and at different stages of demographic aging, are compared and contrasted: Canada, Japan, and Korea. The paper has three objectives: 1) to outline key changes in population, family, and work in the three countries; 2) to consider how knowledge about these changes, their dynamics and interrelationships, is framed with respect to policy options; and 3) to compare Canada, Japan, and Korea in terms of the framing of policy challenges related to demographic aging. It is found that Canada is joining the longstanding pattern of Japan and Korea of late home-leaving by youth, meaning less effective time in the paid labour force. Little deep connection exists between population aging and economic productivity or labour force shortages. Differential labour market participation of women mediates the effects of population aging.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.215
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.292
GPT teacher head0.452
Teacher spread0.160 · 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

Citations8
Published2009
Admission routes2
Has abstractyes

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