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Record W1538886093

Changing Income Inequality and the Elderly in Canada 1991-1996: Provincial Metropolitan and Local Dimensions

2001· article· en· W1538886093 on OpenAlexaboutno aff
Eric G. Moore, Michael A. Pacey

Bibliographic record

VenueSocial and Economic Dimensions of an Aging Population Research Papers · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic inequalityInequalityMetropolitan areaIncome inequality metricsSocial inequalityDemographic economicsPopulationIncome distributionEconomicsDevelopment economicsGeographySociologyDemography
DOInot available

Abstract

fetched live from OpenAlex

Recently, much attention has been given to income inequality in industrialized societies, in part because of the empirical evidence linking high levels of income inequality with high mortality, morbidity and other social ills (Wilkinson, 1996). Analyses of these relations originally focused on national figures, but more recent work has explored these linkages at subnational scales -- for state, provincial and metropolitan entities. At the same time, other studies have documented the recent increases in income inequality during the 1990s in Canada, which raises further questions about the dynamics of the relation between income inequality and its social consequences. In this paper we explore additional dimensions of the structure and change of income inequality in Canada between 1991 and 1996. We examine changing income inequality for the population over 65 as well as for the population as a whole, demonstrating that increases in income inequality are concentrated among those in the labour force years and that there has been little change (even some decline) in income inequality among the elderly. From a geographical perspective, increasing income inequality is significantly a large metropolitan issue and, as such, has a lesser impact on seniors as seniors are relatively more concentrated in smaller urban and rural areas. The fact that income inequality can change quite rapidly at the small area level raises some questions about the links to population health. Population health tends to be cumulative and reflects longer term rather than short-term circumstances. The empirical linkages need significantly more exploration to assess the mechanisms which underlie the observed relationships.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.049
GPT teacher head0.349
Teacher spread0.300 · 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.

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

Citations2
Published2001
Admission routes1
Has abstractyes

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