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Record W2172253503 · doi:10.1017/s1748499500000178

Income Inequality over the Later-Life Course: a Comparative Analysis of Seven OECD Countries

2006· preprint· en· W2172253503 on OpenAlex
Robert L. Brown, Steven G. Prus

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAnnals of Actuarial Science · 2006
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsCarleton UniversityUniversity of Waterloo
Fundersnot available
KeywordsInequalityEconomic inequalityDemographic economicsEconomicsIncome distributionGovernment (linguistics)Life course approachOld Age SecurityIncome inequality metricsDevelopment economicsSociologyDemographyPopulationResearch methodologyPsychology

Abstract

fetched live from OpenAlex

ABSTRACT This paper examines income inequality over stages of the later-life course (age 45 and older) and systems which can be used to mitigate this inequality. Two hypotheses are tested: (1) levels of income inequality decline during old age because public benefits are more equally distributed than work income; and (2) because of the progressive nature of government benefits, countries with stronger public income security programmes are better able to reduce income inequalities during old age. The analysis is performed by comparing age groups within seven OECD countries (Canada, Germany, the Netherlands, Norway, Sweden, the United Kingdom, and the United States of America) using Luxembourg Income Study data from around 2000. Both hypotheses are supported. Several conclusions are drawn from the findings.

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.

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)0.012
metaresearch head score (Gemma)0.001
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 score0.505
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.429
Teacher spread0.317 · 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