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Record W2052441748 · doi:10.3138/ijcs.47.87

Income Inequality and Health Trajectories from Mid-Life to Later Life: Are Canadian and American Differences Widening?

2013· article· en· W2052441748 on OpenAlexvenueaboutno aff
Susan A. McDaniel, Amber Gazso

Bibliographic record

VenueInternational Journal of Canadian Studies · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyLife course approachInequalityWelfareDemographic economicsEconomic inequalityAffect (linguistics)Income distributionDevelopment economicsEconomicsEconomic growthSociologyPsychologyDemographySocial psychologyPopulation

Abstract

fetched live from OpenAlex

Abstract: It is well known that policies and welfare regimes differentially affect the aging process, health trajectories over the life course, and indeed life expectancy. Growing income inequalities are also understood to have health and well-being implications. The focus in this paper is on the effects of growing income inequalities on the health and well-being of those in mid-life as they age in two neighbouring countries, Canada and the United States. The authors rely on a comparative multi-method approach informed by a life course perspective. Placing the trajectories of synthetic cohorts in the two countries in the contexts of contrasting welfare policy regimes, the authors examine the relative effects of growing income inequalities on well-being as people move into their later years. By juxtaposing the effects of long-term policies and growing income inequalities with the life course process of aging, the authors can hypothesize what the health and well-being prospects may be for those who will soon be in their older years in the two countries.

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 score0.120
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.112
GPT teacher head0.447
Teacher spread0.335 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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