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Record W2621181912 · doi:10.1177/0164027500223001

Income Inequality as a Canadian Cohort Ages

2000· article· en· W2621181912 on OpenAlexaffabout
Steven G. Prus

Bibliographic record

VenueResearch on Aging · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEarningsGini coefficientTotal personal incomeInequalityEconomicsEconomic inequalityDemographic economicsIncome distributionCohortWelfareDistribution (mathematics)Welfare statePensionPersonal incomeIncome inequality metricsHousehold incomeCohort effectDependency ratioNet national incomeDemographyEconomic growthGeographyGross incomePublic economicsPolitical scienceSociologyPopulationMedicine

Abstract

fetched live from OpenAlex

Survey of Consumer Finances cross-sectional data from 1973 to 1996 are used in this article to examine Canadian trends in income inequality over the middle and later stages of the life course of a synthetic cohort born between 1922 and 1926. Using Gini coefficients, the findings show that income inequality decreases within a cohort as it grows old; that is, the Canadian retirement income system smoothes out (levels) the distribution of income in later life. The observed decrease in inequality corresponds with a decrease in income from earnings and an increase in dependency on state benefits. The progressive nature of public pension programs in Canada increases the relative income share and the average income of the poorest seniors. Moreover, cross-national comparisons of income inequality show that Canada exhibits a more equal distribution of income in old age compared to countries with similar old-age welfare systems, such as the United States.

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)0.004
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 score0.013
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.259
GPT teacher head0.615
Teacher spread0.356 · 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

Citations28
Published2000
Admission routes2
Has abstractyes

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